library(rags2ridges)
library(ggplot2)

0. Data Preparation

data.horizontal = readRDS("data/derived/data_horizontal_joined.rds")
data.numeric = as.matrix(data.horizontal[, 1:701])
data.Y = data.horizontal$vital_status
group.mRSG = 1:470
group.R2Gn = 471:668
group.RPPA = 669:701

1. Wholistic analysis by omic

A. RPPAArray

data.RPPA = data.numeric[, group.RPPA]

LambdaMax is reduced from 1000 to 10 as all optLambda values are < 10.

Unused 3, 5 and 10-fold CV are disabled and presented in document 03b.

set.seed(42)
opt.RPPA = optPenalty.kCVauto(Y = data.RPPA, lambdaMin = 1e-11, lambdaMax = 10)
#opt.RPPA.10 = optPenalty.kCVauto(Y = data.RPPA, lambdaMin = 1e-11, lambdaMax = 1000, fold = 10)
#opt.RPPA.5 = optPenalty.kCVauto(Y = data.RPPA, lambdaMin = 1e-11, lambdaMax = 1000, fold = 5)
#opt.RPPA.3 = optPenalty.kCVauto(Y = data.RPPA, lambdaMin = 1e-11, lambdaMax = 1000, fold = 3)

#setNames(c(3,5,10,43), c(opt.RPPA.3$optLambda, opt.RPPA.5$optLambda, opt.RPPA.10$optLambda, opt.RPPA$optLambda))
opt.RPPA$optLambda
## [1] 0.0005458799
edgeHeat(opt.RPPA$optPrec, diag = F, textsize = 7)
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

#P = ridgeP(covML(data.RPPA), lambda = 0.001)
#test = covML(data.RPPA)
CNplot(covML(data.RPPA),
       lambdaMin = 1e-11,
       lambdaMax = 1000,
       step = 5000,
       Iaids = T,
       vertical = T,
       value = opt.RPPA$optLambda)
## Perform input checks... 
## Calculating spectral condition numbers... 
## Calculating interpretational aids... 
## Plotting...

#heatmaps.RPPA = vector(length = 9)
#for (i in (seq(0.1, 0.9, 0.1))) {
#  heatmaps.RPPA[10*i] = 
#    edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
#             diag = F, textsize = 7,
#             main =  paste("False Discovery Rate cutoff:", i))
#}

## I have to do the below, otherwise edgeHeat maps do not show up.
i = 0.1
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 7,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  235 
## - Corresponding to 44.51 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.2
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 7,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  231 
## - Corresponding to 43.75 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.3
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 7,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  143 
## - Corresponding to 27.08 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.4
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 7,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  143 
## - Corresponding to 27.08 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.5
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 7,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  120 
## - Corresponding to 22.73 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.6
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 7,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  74 
## - Corresponding to 14.02 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.7
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 7,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  49 
## - Corresponding to 9.28 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.8
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 7,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  27 
## - Corresponding to 5.11 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.9
edgeHeat(M = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 7,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  26 
## - Corresponding to 4.92 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

#P0 = sparsify(P, threshold = "localFDR")
P0.RPPA = sparsify(opt.RPPA$optPrec, threshold = "localFDR", FDRcut=0.9)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  26 
## - Corresponding to 4.92 % of possible edges 
## 
#edgeHeat(P0.RPPA$sparseParCor, diag = F, textsize = 7)
#dev.new(width = 10, height = 10, unit = "in", noRstudioGD = F)
set.seed(42)
Ugraph(P0.RPPA$sparseParCor, type = "fancy", Vsize = 15, Vcex = .5,
       cut = 0.5,
       lay = "layout_with_fr", prune = T,
       main = "RPPA Array data\nFDRcutoff at 0.9, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##             [,1]       [,2]
##  [1,] -1.5479108  0.7458901
##  [2,]  0.4028637  0.7202271
##  [3,] -1.5105063  2.8475053
##  [4,]  0.5670638  2.3929733
##  [5,]  1.1082156  0.1850464
##  [6,]  1.1386108  1.4869290
##  [7,] -0.2561579  1.2932066
##  [8,]  0.2952780 -0.7508837
##  [9,]  1.6798781  2.5247950
## [10,] -0.4124558  0.1002414
## [11,] -1.9197119 -1.8282442
## [12,]  0.2057263  0.1630035
## [13,] -0.4324510  1.8766875
## [14,] -2.6393477 -1.1574446
#Ugraph(P0.RPPA$sparseParCor, type = "fancy", Vsize = 15, Vcex = .5,
#       cut = 0.5,
#       lay = "layout_in_circle", prune = T,
#       main = "RPPA Array data\nFDRcutoff at 0.9, Strong Edge cutoff at 0.5")
GGM.RPPA = as.data.frame(GGMnetworkStats(P0.RPPA$sparseParCor, as.table = T))
## Warning in log(det(S[-j, -j] - S[-j, j, drop = FALSE] %*% S[j, -j, drop =
## FALSE]/S[j, : NaNs produced
GGM.RPPA.order = GGM.RPPA[order(GGM.RPPA$degree, decreasing = T), ]

GGM.RPPA.order[1:10,]
##             degree betweenness  closeness eigenCentrality nNeg nPos  mutualInfo
## IGFBP2.RPPA      7    8.850000 0.06666667       1.0000000    7    0 -0.99348219
## PDCD4.RPPA       6    9.400000 0.06250000       0.8081473    6    0 -0.41366528
## GAPDH.RPPA       6    5.200000 0.05555556       0.8364804    6    0 -1.17208844
## FASN.RPPA        5    7.283333 0.05555556       0.6505305    5    0 -0.68789464
## MYH11.RPPA       5    3.200000 0.05555556       0.7522806    5    0 -0.81135162
## TFRC.RPPA        5   12.400000 0.05882353       0.6487760    5    0  0.35208392
## GATA3.RPPA       4    4.250000 0.05263158       0.4783506    4    0         NaN
## ATM.RPPA         4    1.416667 0.05263158       0.6626447    4    0 -0.08079784
## TTF1.RPPA        3    0.000000 0.04545455       0.5295691    3    0 -0.77092600
## G6PD.RPPA        2    0.000000 0.04545455       0.3364328    2    0  0.53890169
##               variance partialVar
## IGFBP2.RPPA  0.3702850          1
## PDCD4.RPPA   0.6612222          1
## GAPDH.RPPA   0.3097194          1
## FASN.RPPA    0.5026332          1
## MYH11.RPPA   0.4442572          1
## TFRC.RPPA    1.4220279          1
## GATA3.RPPA  -0.4200167          1
## ATM.RPPA     0.9223801          1
## TTF1.RPPA    0.4625845          1
## G6PD.RPPA    1.7141232          1
ggplot(GGM.RPPA.order, aes(x = reorder(rownames(GGM.RPPA.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.RPPA.order$degree), linetype = "dashed", color = "red")  +
  geom_hline(yintercept = 1, linetype = "dashed", color = "blue")  +
  scale_x_discrete(name = "RPPA Array", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-1")

B. RNASeq2GeneNorm

data.R2Gn = data.numeric[, group.R2Gn]
set.seed(42)
opt.R2Gn = optPenalty.kCVauto(Y = data.R2Gn, lambdaMin = 1e-11, lambdaMax = 10)
#opt.R2Gn.10 = optPenalty.kCVauto(Y = data.R2Gn, lambdaMin = 1e-11, lambdaMax = 1000, fold = 10)
#opt.R2Gn.5 = optPenalty.kCVauto(Y = data.R2Gn, lambdaMin = 1e-11, lambdaMax = 1000, fold = 5)
#opt.R2Gn.3 = optPenalty.kCVauto(Y = data.R2Gn, lambdaMin = 1e-11, lambdaMax = 1000, fold = 3)

#setNames(c(3,5,10,43), c(opt.R2Gn.3$optLambda, opt.R2Gn.5$optLambda, opt.R2Gn.10$optLambda, opt.R2Gn$optLambda))
opt.R2Gn$optLambda
## [1] 0.2326298
edgeHeat(opt.R2Gn$optPrec, diag = F, textsize = 1)
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

#P.R2Gn = ridgeP(covML(data.R2Gn), lambda = opt.R2Gn$optLambda)
CNplot(covML(data.R2Gn),
       lambdaMin = 1e-11,
       lambdaMax = 1000,
       step = 5000,
       Iaids = T,
       vertical = T,
       value = opt.R2Gn$optLambda)
## Perform input checks... 
## Calculating spectral condition numbers... 
## Calculating interpretational aids... 
## Plotting...

i = 0.1
edgeHeat(M = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  3287 
## - Corresponding to 16.85 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.5
edgeHeat(M = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  3285 
## - Corresponding to 16.84 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.7
edgeHeat(M = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  3131 
## - Corresponding to 16.05 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.9
edgeHeat(M = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  2511 
## - Corresponding to 12.87 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.999
edgeHeat(M = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  1540 
## - Corresponding to 7.9 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.999999
edgeHeat(M = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  1057 
## - Corresponding to 5.42 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 1-1e-10
edgeHeat(M = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  705 
## - Corresponding to 3.61 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

Smallest possible FDRcut:

#P0.R2Gn = sparsify(P.R2Gn, threshold = "localFDR")
P0.R2Gn.min = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=1-1e-14)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  603 
## - Corresponding to 3.09 % of possible edges 
## 
#dev.new(width = 20, height = 20, unit = "in", noRstudioGD = F)
set.seed(42)
Ugraph(P0.R2Gn.min$sparseParCor, type = "fancy", lay = "layout_with_fr",
       Vsize = 5, Vcex = .45, prune = T, cut = 0.5,
       main = "RNASeq2GeneNorm data\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##                [,1]        [,2]
##   [1,]  1.734183237 -4.52514658
##   [2,]  0.187754883 -5.79499701
##   [3,]  2.641173238  0.55322908
##   [4,] -2.638602483 -0.77703143
##   [5,]  1.150737294 -2.89606430
##   [6,] -0.836133566 -3.52230963
##   [7,] -0.044278251 -2.31349045
##   [8,]  0.007581762  1.83571803
##   [9,]  1.338881693  1.23107667
##  [10,] -0.437822039  4.16731768
##  [11,]  0.784602010 -3.37550872
##  [12,] -2.187820344 -6.99131445
##  [13,]  0.379668303 -0.34942823
##  [14,] -1.434690173 -5.63822022
##  [15,] -1.285245939  0.82321549
##  [16,]  2.430335007 -0.52389937
##  [17,] -0.281420424 -7.64483828
##  [18,]  0.882799717  0.59513840
##  [19,]  0.717046115 -2.95308084
##  [20,] -3.546965174 -1.61422108
##  [21,] -2.827530468 -6.03414478
##  [22,]  7.361957745 -5.31812681
##  [23,]  6.396660888 -6.28068886
##  [24,] -4.271808899  1.81250327
##  [25,]  0.662383245 -1.62350226
##  [26,] -4.428564821 -5.48161601
##  [27,]  1.042613104 -3.25287706
##  [28,]  2.813350211 -5.39106920
##  [29,]  7.887556538 -3.11103814
##  [30,]  2.019986964 -1.66200788
##  [31,]  3.836535334  4.01155736
##  [32,]  3.296264316 -4.63849118
##  [33,]  2.100404963  2.92161147
##  [34,] -3.931202822 -4.65406074
##  [35,] -2.061058751  3.54446818
##  [36,]  6.223492357  0.88253785
##  [37,]  1.626868902 -1.70237272
##  [38,]  2.757949685  2.45150248
##  [39,]  7.886403603 -3.75557154
##  [40,]  0.307707168 -4.30409695
##  [41,]  3.757015505 -0.78846722
##  [42,]  0.230256254  2.81021062
##  [43,]  1.514424252 -2.97663659
##  [44,]  6.144805883 -5.59316554
##  [45,]  3.598793821  1.17084830
##  [46,]  1.637040355 -5.49258987
##  [47,]  0.475504485 -2.40134990
##  [48,]  0.879764700 -0.64488324
##  [49,] -0.059042848 -1.89897013
##  [50,]  2.302391541 -0.93921306
##  [51,] -1.273647691 -1.69132040
##  [52,] -2.780583087  1.31318678
##  [53,]  1.007030082  2.25470119
##  [54,]  7.195825733 -4.69806671
##  [55,]  7.970541337 -2.44420367
##  [56,]  1.167168105 -2.32455851
##  [57,]  2.323238408 -5.79527211
##  [58,]  3.648914570 -1.61267241
##  [59,]  0.759271798 -0.75049077
##  [60,]  7.004036835 -4.00013633
##  [61,]  1.591133837 -4.99689321
##  [62,]  7.399422974 -1.21123110
##  [63,]  0.230396323 -1.78361547
##  [64,] -1.050182519 -0.01760538
##  [65,]  1.586131551 -2.49346435
##  [66,]  2.122102160 -4.06924767
##  [67,] -0.027644040 -0.86106744
##  [68,] -2.088749183 -1.36182464
##  [69,]  1.810561409  2.08040987
##  [70,] -4.001628743 -0.98762795
##  [71,] -2.365557261  1.78401619
##  [72,]  0.536228884 -7.82545926
##  [73,]  1.231075008  3.99261398
##  [74,]  2.013259015  0.89696820
##  [75,]  1.803400427  0.05710434
##  [76,]  7.766288803 -4.41329093
##  [77,]  1.294726490 -1.02235807
##  [78,]  3.672599424 -2.06899349
##  [79,] -1.357178588  0.23350687
##  [80,] -0.141142539  0.27595889
##  [81,]  7.265260849 -2.42708854
##  [82,]  5.808584489 -1.64755706
##  [83,]  5.206278766 -0.03164990
##  [84,]  3.339758296  3.04458630
##  [85,]  1.453872978 -1.89868191
##  [86,]  3.326411172 -3.52533521
##  [87,] -1.281319836  3.93889721
##  [88,] -2.764189904 -3.57038591
##  [89,] -0.638028853 -2.95990608
##  [90,]  2.437551395  0.17761034
##  [91,]  2.534466055 -1.53685749
##  [92,]  6.585230855 -4.96725732
##  [93,]  2.467053564 -3.37815746
##  [94,] -1.949140944 -4.41216124
##  [95,]  2.694710102 -2.06111246
##  [96,]  3.500739370 -2.90935166
##  [97,] -2.482087355 -0.29333847
##  [98,] -2.844788490 -2.22001124
##  [99,] -4.718982067  1.05583422
## [100,]  1.261098219 -0.21186879
## [101,]  1.018176674 -1.58539686
## [102,] -2.182249244  0.16057209
## [103,] -1.382605999 -2.59402426
## [104,]  3.843736133 -5.54822322
## [105,]  2.225763425 -2.61730878
## [106,]  3.059246603 -0.36797989
## [107,]  0.976699024 -4.06922045
## [108,] -1.457858265 -7.36953684
## [109,]  3.052371551 -1.18837643
## [110,]  0.264604338 -2.17261603
## [111,] -1.331484062 -3.50807436
## [112,]  1.489003947 -2.20771029
## [113,] -4.362095387 -3.83248720
## [114,]  1.347674848 -6.89846940
## [115,]  1.797147290 -0.75677614
## [116,]  0.834823677 -1.30727962
## [117,]  0.863047661 -1.95897865
## [118,]  6.867964238 -5.81772967
## [119,] -0.329895281  2.36299975
## [120,]  0.274766264 -1.24340464
## [121,] -0.672761136 -0.90346316
## [122,]  1.579031736 -1.27514605
## [123,]  0.879755903 -2.54512674
## [124,]  0.225210882 -3.24792770
## [125,]  1.979088436 -2.78444112
## [126,] -0.486851981 -3.86978951
## [127,]  7.647255583 -1.79764059
## [128,] -3.372571624 -5.44181712
## [129,] -1.574602283 -2.10270063
## [130,]  5.326451729 -6.79766945
## [131,]  0.177443641  0.64687601
## [132,] -2.283346517 -2.19758016
## [133,] -0.422031066 -0.49980201
## [134,] -5.016746983  0.23158176
## [135,] -1.859789180  2.11792595
## [136,]  3.324498316 -6.04928835
## [137,]  7.191857522 -3.27611071
## [138,] -2.464094482 -2.83020052
## [139,]  0.031594375 -3.00722305
## [140,]  0.043516612 -2.62598756
## [141,]  5.815344796 -6.43640156
#Ugraph(P0.R2Gn.min$sparseParCor, type = "fancy", lay = "layout_in_circle",
#       Vsize = 5, Vcex = .5, prune = T, cut = 0.5,
#       main = "RNASeq2GeneNorm data\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5")
GGM.R2Gn.min = as.data.frame(GGMnetworkStats(P0.R2Gn.min$sparseParCor, as.table = T))
GGM.R2Gn.min.order = GGM.R2Gn.min[order(GGM.R2Gn.min$degree, decreasing = T), ]

#Output top 5%
GGM.R2Gn.min.order[1:round(nrow(GGM.R2Gn.min.order) * 0.05), ]
##             degree betweenness   closeness eigenCentrality nNeg nPos mutualInfo
## GAPDH.R2Gn      72   3220.2971 0.004784689       0.9679823   39   33  1.3162098
## SQSTM1.R2Gn     63   1610.4899 0.004504505       1.0000000   38   25  0.8998836
## FN1.R2Gn        54   1316.7827 0.004366812       0.8736875   34   20  1.0721061
## EEF2.R2Gn       48    898.9281 0.004219409       0.8644423   33   15  0.6452620
## HSPA1A.R2Gn     45   1078.3335 0.004132231       0.6867476   26   19  0.4644268
## IGFBP2.R2Gn     41    716.5096 0.004166667       0.8230592   24   17  0.2548038
## RPS6.R2Gn       41    777.9986 0.004115226       0.7749444   23   18  0.3923880
## SYP.R2Gn        35    568.3648 0.004000000       0.6987146   21   14  0.2315599
## CTNNB1.R2Gn     34    481.7430 0.004000000       0.6849627   15   19  0.3528233
## TGM2.R2Gn       32    521.3536 0.003816794       0.5132311   17   15  0.1252101
##             variance partialVar
## GAPDH.R2Gn  3.729260          1
## SQSTM1.R2Gn 2.459317          1
## FN1.R2Gn    2.921526          1
## EEF2.R2Gn   1.906487          1
## HSPA1A.R2Gn 1.591102          1
## IGFBP2.R2Gn 1.290208          1
## RPS6.R2Gn   1.480512          1
## SYP.R2Gn    1.260565          1
## CTNNB1.R2Gn 1.423080          1
## TGM2.R2Gn   1.133387          1
ggplot(GGM.R2Gn.min.order, aes(x = reorder(rownames(GGM.R2Gn.min.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.R2Gn.min.order$degree), linetype = "dashed", color = "red")  +
  # 10th unit: top 5%
  geom_hline(yintercept = GGM.R2Gn.min.order[10,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-14")

FDRcut 1-1e-6:

#P0.R2Gn = sparsify(P.R2Gn, threshold = "localFDR")
P0.R2Gn.6 = sparsify(opt.R2Gn$optPrec, threshold = "localFDR", FDRcut=1-1e-6)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  1057 
## - Corresponding to 5.42 % of possible edges 
## 
#dev.new(width = 20, height = 20, unit = "in", noRstudioGD = F)
set.seed(42)
Ugraph(P0.R2Gn.6$sparseParCor, type = "fancy", lay = "layout_with_fr",
       Vsize = 5, Vcex = .45, prune = T, cut = 0.5,
       main = "RNASeq2GeneNorm data\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##               [,1]          [,2]
##   [1,] -0.54152185 -2.6823208069
##   [2,] -2.52777380 -0.9635369236
##   [3,] -3.05115118 -4.2528146562
##   [4,] -2.95857085  0.2119218441
##   [5,]  0.77177001  2.0241902056
##   [6,] -1.25826919 -1.9276715923
##   [7,] -1.46704434 -2.4584398868
##   [8,] -0.01644352 -2.2694925021
##   [9,]  1.88107426 -1.3369058166
##  [10,]  0.70227003  0.6537194278
##  [11,]  1.79361320  1.8540098197
##  [12,] -1.57504079 -1.1550441053
##  [13,] -0.55256920 -8.2462418130
##  [14,] -0.68199316  0.0105587104
##  [15,]  1.31064839 -2.8938326668
##  [16,]  0.20789303  0.9945805013
##  [17,]  0.23113071 -0.3722112387
##  [18,]  2.98251684 -2.0040443205
##  [19,] -0.30318210 -0.0457155418
##  [20,] -1.19465049 -0.7169467463
##  [21,] -4.47290978  0.9286987221
##  [22,] -2.86993539 -3.0618902255
##  [23,] -1.87386037 -5.4775653196
##  [24,] -6.80893003 -4.4201979000
##  [25,] -1.82940988 -2.9495552763
##  [26,] -5.13868487 -6.3805145592
##  [27,] -2.47785370  2.9271206834
##  [28,]  0.09530713 -1.2930458476
##  [29,] -1.32116678  1.3963664081
##  [30,] -0.33929751 -1.5718390303
##  [31,] -1.72998663 -3.7648302178
##  [32,] -1.95456886 -5.0583717787
##  [33,] -1.07886360 -0.2433476557
##  [34,]  1.21739957  3.4123583074
##  [35,] -0.03617951  0.0006653903
##  [36,]  3.11565080 -0.3101869932
##  [37,] -0.52942452 -5.2932545909
##  [38,] -0.24301804  2.5318501893
##  [39,] -0.41966266 -3.9781673638
##  [40,] -0.56044392 -1.9045855333
##  [41,]  2.98683827 -2.7721897195
##  [42,]  0.13354246 -5.0826360771
##  [43,]  0.29914527 -2.1106055036
##  [44,] -2.34578189 -0.5641559609
##  [45,] -1.62061441  2.0141771405
##  [46,] -0.93381569 -2.0159926933
##  [47,] -6.55148983 -3.8656619486
##  [48,]  2.48196145 -1.2651950180
##  [49,] -2.49506535 -4.2015907086
##  [50,]  0.26562212 -1.6787891582
##  [51,] -0.11795628 -0.8310228538
##  [52,] -1.19193043 -1.1537200177
##  [53,] -0.46205555 -0.6538650705
##  [54,] -1.47106555 -8.2627651612
##  [55,]  4.24138739  3.5975322019
##  [56,]  0.24879690 -3.0786944686
##  [57,] -1.24208901  2.7493399030
##  [58,] -0.68098739  1.4652636658
##  [59,] -2.80951341  1.9310559211
##  [60,] -3.89162157 -2.7526294246
##  [61,] -1.05622844 -5.9625201651
##  [62,]  3.21680685  0.4605173074
##  [63,] -0.34252056  0.7742189183
##  [64,] -0.75069256 -1.4889781471
##  [65,] -2.64766260 -5.3806136977
##  [66,]  1.32274812 -3.3675485667
##  [67,]  0.79128900 -1.1224682614
##  [68,] -4.14037647  0.0647679289
##  [69,] -2.07840797 -1.6983500542
##  [70,] -6.98987960 -3.0289175708
##  [71,] -0.67312227 -1.1121871636
##  [72,]  1.25425598 -1.4892856357
##  [73,] -0.35599800 -1.3051768950
##  [74,] -1.29272200 -2.8823637510
##  [75,]  1.19378453 -1.1274965103
##  [76,] -1.88101594  4.6381942949
##  [77,]  0.77338735 -0.6619381221
##  [78,] -2.47655152 -6.4087288040
##  [79,]  1.90480742  0.7639775177
##  [80,]  2.32751458  0.6888319314
##  [81,]  2.44532612  3.9103923661
##  [82,] -2.66677269 -1.9521229006
##  [83,] -3.91051014 -0.6369557498
##  [84,]  0.28582715  3.2972232391
##  [85,] -3.54335215 -5.8653674237
##  [86,] -0.87146060  0.5429376428
##  [87,]  0.79703896  0.1614962640
##  [88,] -5.72147738 -5.3678421398
##  [89,]  5.73136797 -1.8096359041
##  [90,] -1.62079179 -0.7734014230
##  [91,] -2.41095358 -0.0840959274
##  [92,]  1.65197729 -0.8232167029
##  [93,]  1.50275383 -1.7033391308
##  [94,] -6.22178429 -4.6512981765
##  [95,] -0.01419192 -3.4595637009
##  [96,]  4.20569029 -4.8005331415
##  [97,]  1.88783310  3.2190467238
##  [98,]  1.61093260 -2.1235585474
##  [99,]  1.15530272 -5.0175504792
## [100,] -1.27925113 -1.4054450396
## [101,]  5.68083930 -0.8689934738
## [102,]  0.59140492 -3.0707913772
## [103,]  0.29489957  1.4626839720
## [104,] -3.62488858 -1.7993539369
## [105,] -0.11825778 -1.1128329498
## [106,] -2.59737334 -1.4846197774
## [107,]  0.09750354 -1.7803741189
## [108,] -7.15666422 -1.7387883539
## [109,] -1.96145816 -1.2258137423
## [110,]  1.62792206 -2.8118455040
## [111,] -1.20437507  0.2060277829
## [112,] -4.06301224  1.3998501052
## [113,] -2.25507520 -2.2971490491
## [114,]  2.08363693  0.1280508913
## [115,] -3.19753529 -1.2259728031
## [116,]  1.01385901 -3.6024542468
## [117,]  1.03583880 -1.8844715362
## [118,]  0.56500529 -1.6796871408
## [119,]  2.13440862 -2.7769275708
## [120,]  3.51821601  4.2884951834
## [121,] -2.00389195  0.2613315964
## [122,]  4.81595674  2.8790419751
## [123,] -5.52449180 -1.2519286559
## [124,] -3.96213008 -5.4312565076
## [125,]  0.34941861 -8.0579727156
## [126,] -0.29908740 -2.3403212844
## [127,] -2.49557496  0.4270806578
## [128,] -6.20022833  1.0154493420
## [129,] -3.03236844 -0.5995868287
## [130,] -1.21482706 -4.4226533003
## [131,]  0.55552673 -4.7713096389
## [132,]  1.33029235 -0.5465351731
## [133,] -0.76180523 -0.7582361408
## [134,] -1.57069802  0.4812795790
## [135,] -0.95197681 -0.6424997540
## [136,] -0.81524621 -4.1965403178
## [137,] -0.21907953 -4.4790369294
## [138,] -1.48909466 -0.1524450945
## [139,] -0.96981365 -1.7081745475
## [140,] -0.94318144 -1.3007555819
## [141,] -1.02067079  3.5921937998
## [142,] -7.19607299 -2.4547776491
## [143,] -2.10502718  1.2054471822
## [144,] -3.07111845 -6.2098303825
## [145,]  5.59897609  0.1129115316
## [146,]  0.34154150 -1.2581283900
## [147,] -0.65362164 -0.3267062350
## [148,] -0.12704383 -0.4956486219
## [149,] -4.92774144 -0.3883788376
## [150,] -0.48399089  5.0538272899
## [151,]  0.31743639 -0.8987091347
## [152,] -0.98367407 -3.3769310106
## [153,]  0.73660784 -2.4919698847
## [154,] -0.88141404 -2.9538753068
## [155,] -4.81621089  0.3836162200
## [156,] -6.37001615 -5.2412257207
## [157,]  3.53134584 -1.3125198669
## [158,]  1.31447181  0.1078887007
## [159,] -3.59773339 -4.1503755350
## [160,] -0.88975851 -2.5694290561
## [161,] -5.68199536  1.8730719011
## [162,] -2.17007197 -2.7082810599
## [163,]  3.54839980 -5.6599256062
## [164,] -4.96289378  2.6463412417
## [165,]  0.41091557 -0.1323433783
## [166,]  2.39187855 -0.6056031736
## [167,] -5.42153453 -1.8935913797
## [168,]  1.17270275  1.5934372433
## [169,] -5.25087446 -2.5711102249
## [170,] -5.73090333 -5.9594247452
## [171,]  2.10286762 -4.7523998603
## [172,] -1.78090605 -1.9311695168
## [173,] -0.05846189 -2.1083750323
## [174,] -7.11129446 -3.6086269930
#Ugraph(P0.R2Gn.6$sparseParCor, type = "fancy", lay = "layout_in_circle",
#       Vsize = 5, Vcex = .5, prune = T, cut = 0.5,
#       main = "RNASeq2GeneNorm data\nFDRcutoff at .999999, Strong Edge cutoff at 0.5")
GGM.R2Gn.6 = as.data.frame(GGMnetworkStats(P0.R2Gn.6$sparseParCor, as.table = T))
GGM.R2Gn.6.order = GGM.R2Gn.6[order(GGM.R2Gn.6$degree, decreasing = T), ]

#Output top 5%
GGM.R2Gn.6.order[1:round(nrow(GGM.R2Gn.6.order) * 0.05), ]
##             degree betweenness   closeness eigenCentrality nNeg nPos mutualInfo
## GAPDH.R2Gn     112   4702.3715 0.004273504       1.0000000   56   56  1.3594455
## SQSTM1.R2Gn     81   1534.4196 0.003690037       0.8962939   47   34  0.9259119
## FN1.R2Gn        74   1602.9597 0.003636364       0.8018610   46   28  1.0805192
## HSPA1A.R2Gn     70   1370.0239 0.003597122       0.7250416   38   32  0.4884313
## EEF2.R2Gn       63    762.6757 0.003521127       0.7983647   38   25  0.6501149
## IGFBP2.R2Gn     62   1199.7673 0.003521127       0.7852547   35   27  0.2716628
## SYP.R2Gn        61   1285.7088 0.003436426       0.6896222   35   26  0.2486661
## RPS6.R2Gn       60   1146.1182 0.003460208       0.7277443   34   26  0.4176465
## TGM2.R2Gn       55   1075.7374 0.003355705       0.6095397   31   24  0.1507605
## CTNNB1.R2Gn     50    324.2004 0.003344482       0.7179654   23   27  0.3197247
##             variance partialVar
## GAPDH.R2Gn  3.894034          1
## SQSTM1.R2Gn 2.524169          1
## FN1.R2Gn    2.946209          1
## HSPA1A.R2Gn 1.629758          1
## EEF2.R2Gn   1.915761          1
## IGFBP2.R2Gn 1.312144          1
## SYP.R2Gn    1.282314          1
## RPS6.R2Gn   1.518384          1
## TGM2.R2Gn   1.162718          1
## CTNNB1.R2Gn 1.376749          1
ggplot(GGM.R2Gn.6.order, aes(x = reorder(rownames(GGM.R2Gn.6.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.R2Gn.6.order$degree), linetype = "dashed", color = "red")  +
  # 10th unit: top 5%
  geom_hline(yintercept = GGM.R2Gn.6.order[10,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-6")

C. miRNASeqGene

data.mRSG = data.numeric[, group.mRSG]
set.seed(42)
opt.mRSG = optPenalty.kCVauto(Y = data.mRSG, lambdaMin = 1e-11, lambdaMax = 10)
#opt.mRSG.10 = optPenalty.kCVauto(Y = data.mRSG, lambdaMin = 1e-11, lambdaMax = 1000, fold = 10)
#opt.mRSG.5 = optPenalty.kCVauto(Y = data.mRSG, lambdaMin = 1e-11, lambdaMax = 1000, fold = 5)
#opt.mRSG.3 = optPenalty.kCVauto(Y = data.mRSG, lambdaMin = 1e-11, lambdaMax = 1000, fold = 3)

#setNames(c(3,5,10,43), c(opt.mRSG.3$optLambda, opt.mRSG.5$optLambda, opt.mRSG.10$optLambda, opt.mRSG$optLambda))
opt.mRSG$optLambda
## [1] 3.5931
edgeHeat(opt.mRSG$optPrec, diag = F, textsize = 0.1)
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

CNplot(covML(data.mRSG),
       lambdaMin = 1e-11,
       lambdaMax = 1000,
       step = 5000,
       Iaids = T,
       vertical = T,
       value = opt.mRSG$optLambda)
## Perform input checks... 
## Calculating spectral condition numbers... 
## Calculating interpretational aids... 
## Plotting...

i = 0.1
edgeHeat(M = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  16050 
## - Corresponding to 14.56 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.5
edgeHeat(M = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  16049 
## - Corresponding to 14.56 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.9
edgeHeat(M = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  10494 
## - Corresponding to 9.52 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.999
edgeHeat(M = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  5669 
## - Corresponding to 5.14 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 1-1e-6
edgeHeat(M = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  3091 
## - Corresponding to 2.8 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 1-1e-10
edgeHeat(M = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  1571 
## - Corresponding to 1.43 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

Smallest possible FDRcut:

P0.mRSG.min = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=1-1e-13)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  1164 
## - Corresponding to 1.06 % of possible edges 
## 
#dev.new(width = 20, height = 20, unit = "in", noRStudioGD = F)
set.seed(42)
Ugraph(P0.mRSG.min$sparseParCor, type = "fancy", lay = "layout_with_fr",
       Vsize = 2, Vcex = .3, prune = T, cut = 0.5,
       main = "miRNASeqGene data\nFDRcutoff at 1-1e-13, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##                [,1]         [,2]
##   [1,]  1.693443199  -0.57080262
##   [2,]  2.237906712   2.09902249
##   [3,]  3.407380042  -5.22942842
##   [4,]  1.555401960   8.09915341
##   [5,]  1.709161949   6.81248248
##   [6,]  0.193112853   5.41140659
##   [7,]  0.626934990   7.33240361
##   [8,]  1.062255142  -1.33596998
##   [9,] -4.448723695  -6.34017023
##  [10,] -0.172648992  -2.91921698
##  [11,]  2.929827339  -2.70393521
##  [12,]  2.731830830  -1.31063492
##  [13,]  2.480043823  -2.16720867
##  [14,]  1.506613757   0.78739724
##  [15,]  3.342913606   0.50705997
##  [16,]  3.769793891  -7.91127051
##  [17,]  0.144797115  -1.38872733
##  [18,]  1.523180674  -5.72488991
##  [19,]  3.555983344  -4.59337048
##  [20,]  7.932228615  -1.47770407
##  [21,]  3.876073986  -1.32245826
##  [22,]  4.220382868  11.01844206
##  [23,]  0.922281428  -2.65403836
##  [24,]  0.338775353  -0.48257555
##  [25,] -1.843727463  -7.50665268
##  [26,] -0.245275929  -3.59795509
##  [27,]  0.478563530  -2.14775787
##  [28,] -4.916303015  -1.25334284
##  [29,]  5.270168775   0.71996878
##  [30,]  5.413851056  -2.10286016
##  [31,] -1.074650119   1.00258047
##  [32,]  4.295841529  -1.44737800
##  [33,]  1.235885923  -0.79711259
##  [34,]  0.483524823  11.92959079
##  [35,] -1.755776857  -2.69667657
##  [36,]  0.106001345  -1.23126557
##  [37,]  2.020007889  -2.13964333
##  [38,]  1.954864933   0.81986277
##  [39,]  1.305955754  -1.93140878
##  [40,] -2.560407144  -4.32781082
##  [41,]  2.657876730  -5.61609491
##  [42,]  4.256820591  -2.06689018
##  [43,]  4.185609255  -2.69985537
##  [44,]  2.674719600  11.60794630
##  [45,]  7.877213374  -2.73460091
##  [46,]  7.752747608  -3.25043782
##  [47,]  2.764932109  -3.91847665
##  [48,]  1.135116433  -0.04564735
##  [49,]  1.831904149  -1.18212127
##  [50,]  3.672487656  -4.19031439
##  [51,]  0.725085363  -7.07274673
##  [52,]  0.559091692  -4.19962146
##  [53,]  4.635782268  -0.19043058
##  [54,]  4.976871053  -0.40317870
##  [55,]  5.883221520   2.42843790
##  [56,] -3.805324199  -7.14143008
##  [57,] -0.463975043  -0.40813107
##  [58,]  1.704746789  -0.19973475
##  [59,] -1.505940255   4.09356561
##  [60,]  4.541633935  -4.71061109
##  [61,]  4.642847312  -1.21866239
##  [62,]  3.166927202  -2.98713521
##  [63,]  6.382069150  -2.83345514
##  [64,]  1.451638055  -3.75664978
##  [65,] -0.160331203  -8.48275551
##  [66,] -4.857495091  -7.94506850
##  [67,] -2.977600130   1.74927695
##  [68,]  3.117979787  -1.42110210
##  [69,]  1.755911753  11.94739499
##  [70,]  9.554642481  -0.79680118
##  [71,] -1.370797128   2.19547402
##  [72,]  3.206155709  -0.60653995
##  [73,]  4.381143027   3.07098637
##  [74,]  2.752478935  -0.92508226
##  [75,]  4.600376978  -2.79221010
##  [76,]  2.688718387  -7.84606628
##  [77,]  4.706348662   0.47035676
##  [78,]  3.722117182  -2.96524542
##  [79,]  6.861715440  -2.74050991
##  [80,]  3.392814164  -2.41594243
##  [81,] -1.263001179  -0.25523443
##  [82,] -0.957840415  -3.53071024
##  [83,]  9.290424913  -2.88550368
##  [84,]  6.637910365   0.09805605
##  [85,]  0.282580671   7.84794587
##  [86,]  0.607715259   6.82903672
##  [87,]  1.658184651   7.52617151
##  [88,]  2.011181295   8.07309164
##  [89,]  1.084720155  -7.84262884
##  [90,]  3.870888550  -0.53776579
##  [91,]  3.089024228  -0.24638481
##  [92,]  7.043067346   1.07296584
##  [93,]  4.661463875   2.22487268
##  [94,]  5.604696526  -1.64413980
##  [95,]  1.241780051  -2.63063958
##  [96,]  7.155831118  -0.02139164
##  [97,] -3.041458248  -0.98746340
##  [98,]  4.332788080  -0.74539708
##  [99,]  2.562601394   0.29992003
## [100,] -2.702801326  -3.62890579
## [101,]  4.757957822  -3.57384821
## [102,]  5.318674016  -0.21582119
## [103,] -0.306503185  -1.80105732
## [104,]  2.335515010   1.62400881
## [105,]  3.228864585  11.87412677
## [106,]  2.238078016  -0.01757222
## [107,]  0.766209042   8.09272485
## [108,]  2.154260900   6.01749149
## [109,] -0.220969140   8.36103334
## [110,]  1.708339189  10.08193479
## [111,]  2.320206733  12.18355246
## [112,]  1.706886013   5.04153924
## [113,]  3.680257066  11.38944241
## [114,]  3.874244828  -3.32630792
## [115,] -0.868659975  -8.13433337
## [116,]  2.945516268   1.00790350
## [117,]  4.640814727  -1.05758891
## [118,] -2.898815235   0.04405493
## [119,] -0.579989935  -1.52584987
## [120,] 11.546254989   1.12457254
## [121,]  1.409420971  -1.45182563
## [122,]  1.079349236  -4.89749479
## [123,] -0.002205569  10.29975248
## [124,]  0.107659318   9.61297171
## [125,]  2.619790033   8.49024492
## [126,]  4.064807815  -1.88072447
## [127,] -0.469648172   9.47639283
## [128,]  0.754893463   9.78219618
## [129,]  1.533724072   8.76880223
## [130,] -7.731890653 -10.63190670
## [131,]  2.553572639  -1.80544121
## [132,]  7.637007963  -0.59366945
## [133,]  2.272020802   7.67671130
## [134,]  2.142355656   5.45598138
## [135,] -0.708802397   7.73625379
## [136,] -0.456762688  -3.85651156
## [137,]  1.683480550  -1.38007925
## [138,]  2.541826351  -0.66117754
## [139,]  2.997766036   7.57045921
## [140,] -0.644109662   8.13114046
## [141,]  3.612379558   0.54673109
## [142,]  3.395682491  -1.98482160
## [143,]  6.052100427  -7.04307170
## [144,]  5.790531676  -5.76182106
## [145,]  5.827128240  -7.60365827
## [146,]  6.567793618  -7.02813830
## [147,]  5.425040378  -7.41032057
## [148,]  5.645198893  -7.02292983
## [149,]  5.513668667  -5.24743281
## [150,]  3.542104180 -14.25929494
## [151,]  3.214957578 -10.21575421
## [152,]  5.793915311  -6.14595426
## [153,]  4.930717567  -5.38806309
## [154,]  5.729014031  -7.33260723
## [155,]  6.414362497  -7.30532295
## [156,]  6.132320767  -7.42063184
## [157,]  6.224813429  -6.78382695
## [158,]  6.570006789  -6.72221557
## [159,]  5.201267290  -6.37297894
## [160,]  0.626348251   0.80287623
## [161,]  0.458709470   1.04634835
## [162,] -0.396923465   0.91254269
## [163,]  0.065895725  -0.20758829
## [164,] -0.009743296   0.72287473
## [165,]  0.843042047   0.27758995
## [166,]  0.416367487   0.09223111
## [167,]  0.554479507  -1.05546457
## [168,]  1.249217141   0.34368532
## [169,]  1.487445222   1.05363004
## [170,] -0.046532072   1.16244980
## [171,]  0.731059749   1.73561962
## [172,]  1.151272809  12.06464551
## [173,]  1.513938976   5.90358210
## [174,]  2.178186474   9.53834294
## [175,] -1.185113684   3.30272763
## [176,]  3.474649694  -0.65711384
## [177,] -0.533116194  -1.15764459
## [178,]  3.550514878   0.99227045
## [179,]  6.583120538   2.06572140
## [180,] -3.662881683   3.15187027
## [181,]  1.959515575  -4.15748737
## [182,]  3.544594236  -1.29712855
## [183,] -0.880797308  -3.06522674
## [184,] -0.357119026   0.29930478
## [185,]  1.894490182   1.53473625
## [186,]  0.695487399  -5.46643765
## [187,]  4.871793528  -2.43320663
## [188,]  1.009904588  -3.52529183
## [189,]  2.222657873  -2.73332602
## [190,] -0.675412188  -2.53823794
## [191,] -0.231433075  -4.76433392
## [192,]  2.526087060  -0.13525751
## [193,] -2.978762931  -2.94687907
## [194,]  3.625982865  -0.12089297
## [195,] -1.618413290   1.21595641
## [196,]  2.650432630   7.58538456
## [197,]  0.730032812   4.90822816
## [198,]  1.524041871  -2.13631553
## [199,]  1.544957311   1.82042509
## [200,]  1.056801680   7.10246900
## [201,] -1.399463427  -1.87770210
## [202,] -1.600010259  -4.72401448
## [203,]  0.144541576  -3.27623448
## [204,]  1.850548707  -4.94678203
## [205,] -0.309338808  11.65286150
## [206,]  9.477822551  -0.04472504
## [207,]  6.155125804  -5.01871841
## [208,]  2.844011794   0.01885118
## [209,]  1.112513667   6.16222381
## [210,]  2.046533466  -2.96018731
## [211,]  1.199982913  -3.74148441
## [212,]  2.460075086  -2.96587265
## [213,]  0.623577260  10.60787870
## [214,]  2.541392913  -1.52506846
## [215,] -0.860040405  -1.72733951
## [216,] -0.953536526  -1.33114546
## [217,]  0.454321625  -2.67903351
## [218,]  6.331504710  -3.83099014
## [219,]  2.385118000  -4.20497553
## [220,] -0.940924329  -5.44626448
## [221,]  1.289219269  -9.07911134
## [222,]  9.011017639  -4.03378955
#Ugraph(P0.mRSG.min$sparseParCor, type = "fancy", lay = "layout_in_circle",
#       Vsize = 2, Vcex = .1, prune = T, cut = 0.5,
#       main = "miRNASeqGene data\nFDRcutoff at 1-1e-13, Strong Edge cutoff at 0.5")
GGM.mRSG.min = as.data.frame(GGMnetworkStats(P0.mRSG.min$sparseParCor, as.table = T))
GGM.mRSG.min.order = GGM.mRSG.min[order(GGM.mRSG.min$degree, decreasing = T), ]

#Output top 5%
GGM.mRSG.min.order[1:round(nrow(GGM.mRSG.min.order) * 0.05), ]
##                degree betweenness   closeness eigenCentrality nNeg nPos
## hsa-mir-206        46   3024.5913 0.002304147       1.0000000   20   26
## hsa-mir-329-1      42   3788.8954 0.001855288       0.2068821    3   39
## hsa-mir-1258       38   2389.8497 0.002096436       0.7516007   13   25
## hsa-mir-137        35   2091.5612 0.002092050       0.5821485   20   15
## hsa-mir-135a-2     31    861.9311 0.001964637       0.6824739   17   14
## hsa-mir-873        29    978.3604 0.002008032       0.5695108   15   14
## hsa-mir-1251       28    877.2843 0.001996008       0.4894875    9   19
## hsa-mir-519a-1     27    425.7017 0.001937984       0.7593073   11   16
## hsa-mir-668        27    974.1874 0.001769912       0.1811547    1   26
## hsa-mir-3923       26   1163.8327 0.001941748       0.4080900   13   13
## hsa-mir-1197       23    307.8578 0.001587302       0.1400930    1   22
## hsa-mir-218-1      23   1135.4310 0.001930502       0.3332649    8   15
## hsa-mir-3934       23    329.0913 0.001893939       0.5246196   14    9
## hsa-mir-526b       23   1130.7760 0.002032520       0.6506809    8   15
## hsa-mir-9-3        23    411.3859 0.001956947       0.4853357    8   15
## hsa-mir-122        22    358.2695 0.001919386       0.3919011   17    5
## hsa-mir-488        22   1132.1611 0.001851852       0.4008020    6   16
## hsa-mir-133b       21    195.0829 0.001923077       0.6710586    7   14
## hsa-mir-518c       21    137.8757 0.001872659       0.6254225    5   16
## hsa-mir-520a       21    331.9399 0.001941748       0.6361110    7   14
## hsa-mir-124-3      20    356.7712 0.001949318       0.3930271   11    9
## hsa-mir-153-1      20    651.5283 0.001828154       0.3036387   10   10
## hsa-mir-429        20    361.3372 0.001788909       0.2942850   10   10
## hsa-mir-510        20    238.2183 0.001686341       0.4289051    2   18
##                mutualInfo variance partialVar
## hsa-mir-206    0.03292890 1.033477          1
## hsa-mir-329-1  0.02103435 1.021257          1
## hsa-mir-1258   0.02247154 1.022726          1
## hsa-mir-137    0.02237436 1.022627          1
## hsa-mir-135a-2 0.02351349 1.023792          1
## hsa-mir-873    0.01980087 1.019998          1
## hsa-mir-1251   0.02147339 1.021706          1
## hsa-mir-519a-1 0.01763995 1.017796          1
## hsa-mir-668    0.01319329 1.013281          1
## hsa-mir-3923   0.01784204 1.018002          1
## hsa-mir-1197   0.01172850 1.011798          1
## hsa-mir-218-1  0.01380980 1.013906          1
## hsa-mir-3934   0.01104792 1.011109          1
## hsa-mir-526b   0.01837847 1.018548          1
## hsa-mir-9-3    0.01562936 1.015752          1
## hsa-mir-122    0.01418258 1.014284          1
## hsa-mir-488    0.01150804 1.011575          1
## hsa-mir-133b   0.01022348 1.010276          1
## hsa-mir-518c   0.01146126 1.011527          1
## hsa-mir-520a   0.01381192 1.013908          1
## hsa-mir-124-3  0.01160858 1.011676          1
## hsa-mir-153-1  0.01621961 1.016352          1
## hsa-mir-429    0.01472198 1.014831          1
## hsa-mir-510    0.01805642 1.018220          1
ggplot(GGM.mRSG.min.order, aes(x = reorder(rownames(GGM.mRSG.min.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.mRSG.min.order$degree), linetype = "dashed", color = "red")  +
  # 24th unit: top 5%
  geom_hline(yintercept = GGM.mRSG.min.order[24,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "miRNASeqGene", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-13")

FDRcut 1-1e-6:

P0.mRSG.6 = sparsify(opt.mRSG$optPrec, threshold = "localFDR", FDRcut=1-1e-6)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  3091 
## - Corresponding to 2.8 % of possible edges 
## 
#dev.new(width = 20, height = 20, unit = "in", noRStudioGD = F)
set.seed(42)
Ugraph(P0.mRSG.6$sparseParCor, type = "fancy", lay = "layout_with_fr",
       Vsize = 2, Vcex = .3, prune = T, cut = 0.5,
       main = "miRNASeqGene data\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##                 [,1]         [,2]
##   [1,]  2.0414684168 -8.264597744
##   [2,] -1.1675039128 -0.804889250
##   [3,]  0.3738857418 -0.015780581
##   [4,] -7.6410330380 -0.809465400
##   [5,] -1.1747619636 -2.810894145
##   [6,] -0.0553588813  3.377971773
##   [7,]  0.3556158150  3.430412961
##   [8,]  0.8550540726  2.929747388
##   [9,] -0.0066746246  2.842760487
##  [10,] -1.7573468955 -1.086597035
##  [11,] -3.0743731826  0.119128961
##  [12,]  5.5791247353  0.019243822
##  [13,] -2.8713371630 -2.963344915
##  [14,] -0.6501123290 -0.489293054
##  [15,] -0.8544911024 -0.140814506
##  [16,]  0.3791833849 -1.071472196
##  [17,]  1.0789755654 -0.712184425
##  [18,]  1.1339235668 -1.443850134
##  [19,] -2.2190761068  0.540850912
##  [20,]  0.1381562594 -4.959376622
##  [21,] -1.2479752378 -1.783118714
##  [22,]  5.3333655774  2.537516467
##  [23,] -0.0745066446 -0.774956100
##  [24,] -0.2154185758 -3.111419427
##  [25,] -6.0374036651 -6.208451635
##  [26,] -5.8630824151  2.286409914
##  [27,]  2.7056766352  0.317901032
##  [28,]  4.2881122126 -6.252083270
##  [29,]  0.1333581179 -2.972601754
##  [30,] -0.2400353602  4.494989024
##  [31,] -0.6099603091 -1.945629177
##  [32,]  0.7002497945 -1.856510234
##  [33,]  5.2210908234 -1.748226857
##  [34,]  0.8077229883 -3.805736203
##  [35,] -4.2634883097  4.022959612
##  [36,] -2.6219465219 -1.255522677
##  [37,] -1.4241297913 -0.995201507
##  [38,]  3.7363075378 -4.268025369
##  [39,]  1.9819929165 -0.994949976
##  [40,]  1.6923614249 -1.600121968
##  [41,]  0.5500713221 -1.188848968
##  [42,]  1.3415271045 -2.017360687
##  [43,] -0.9117713617 -0.625916630
##  [44,] -0.9573859298  3.446627176
##  [45,] -2.3625170954 -2.345863661
##  [46,] -0.3901344641 -2.015979868
##  [47,] -0.4971318960 -3.350777275
##  [48,]  1.3050609848  4.287663696
##  [49,] -0.9497960787 -0.391940819
##  [50,] -0.2681666138 -0.587419652
##  [51,] -1.7074125705 -1.975483852
##  [52,] -0.8596130480 -4.058596819
##  [53,]  4.9435163511 -3.079084254
##  [54,] 15.5220345641 -4.839944216
##  [55,] -4.6596152965 -3.248935822
##  [56,]  0.5393476886 -0.703085160
##  [57,]  0.0355622502 -0.057826935
##  [58,]  0.7861810955  4.474663400
##  [59,] -1.6109894781 -5.731145498
##  [60,] -1.4692371715 -4.729888895
##  [61,] -0.2792236238 -2.356392783
##  [62,] -0.5901011139 -1.639751993
##  [63,] -1.1934251111 -0.273454841
##  [64,] -1.2189665635 -3.408148606
##  [65,] -6.9821803786  0.569032430
##  [66,] -7.1553152715 -2.227137887
##  [67,] -7.1328450358 -1.367524868
##  [68,]  1.2097372225 -5.599214458
##  [69,] -2.8111004365 -3.256614771
##  [70,] -2.1292297619 -2.717245801
##  [71,] -4.1705861727 -4.377505560
##  [72,]  1.2387215322 -1.022510500
##  [73,]  1.4923005846 -1.109450542
##  [74,] -0.6042237611 -4.999963897
##  [75,] -1.8984542386 -7.022147214
##  [76,] -2.2063189909 -2.203561608
##  [77,] -0.6569417206 -0.252184140
##  [78,]  0.1009427178 -0.871591006
##  [79,] -4.8294714092  1.288730952
##  [80,]  1.2990234699 -2.834457668
##  [81,] -7.0615453994 -0.415473104
##  [82,]  0.0860617183 -2.229224588
##  [83,] -0.0992084121 -2.616832188
##  [84,] -1.2602226946 -5.017964764
##  [85,] -0.2430583718 -2.943004023
##  [86,] -0.3118160257 -8.518090213
##  [87,] -3.5158535239 -3.065109175
##  [88,] -1.5400679328  0.987154534
##  [89,]  0.4700294908 -1.521581902
##  [90,] -0.4039027199  5.079982147
##  [91,]  6.3927378365 -4.655290648
##  [92,] -3.2929410293 -1.911731213
##  [93,] -5.7747009586 -5.426782462
##  [94,]  2.2337601603  1.547675182
##  [95,]  0.1654252195 -1.441542534
##  [96,] -1.0330749507 -4.393912887
##  [97,]  0.1177007436 -0.652798211
##  [98,]  0.1431657991 -3.325810186
##  [99,] -2.4086044250 -4.688758424
## [100,] -0.0902170349 -3.550410242
## [101,]  0.2848442711 -2.038357626
## [102,] -0.4706244642 -1.410269886
## [103,] -0.6395980633 -2.878165762
## [104,] -0.9253927986 -1.413665674
## [105,]  0.5141657488 -2.799393371
## [106,]  0.6495681115 -0.354423852
## [107,]  3.0877219378 -6.427137969
## [108,]  3.1355083924 -1.219808758
## [109,]  1.8998627465 -0.121590574
## [110,]  0.2284460421  4.627374302
## [111,]  0.4320360889  4.537154650
## [112,]  0.2221997015  2.733261701
## [113,]  0.9491350496  2.674642496
## [114,]  0.6518099839 -8.627015264
## [115,] -5.3857382467 -2.893471842
## [116,]  2.6290820063  5.638039834
## [117,]  0.2440528469 -2.485968443
## [118,] -0.0533674238 -1.547254046
## [119,]  1.4237381739  0.330659707
## [120,]  0.5531226041  1.136820015
## [121,]  1.6160394815 -0.590845992
## [122,] -0.7772932472 -2.312770444
## [123,]  1.5570888376 -2.962788291
## [124,] -3.8812771068 -4.817111214
## [125,]  0.4645870550 -5.515493674
## [126,] -0.9532329371 -3.197070019
## [127,]  1.3184286339 -3.064978857
## [128,] -0.5345626016 -2.583183193
## [129,] -0.9166055715 -3.634812833
## [130,] -0.6332352198 -1.137613111
## [131,] -0.4446501908 -0.868166445
## [132,] -1.3833937935 -2.361845740
## [133,] -0.8910565547  4.309277751
## [134,] -1.1316230754 -2.262072021
## [135,] -0.7856089778  4.084515918
## [136,]  0.5539668706 -2.136386014
## [137,]  1.0051099353  4.003607382
## [138,]  0.3859054227  3.097900311
## [139,]  1.1307625246  4.893642482
## [140,]  0.9872217295  4.599804025
## [141,] -1.1886168818  3.841829832
## [142,] -0.4366360618  4.543182166
## [143,] -0.1658837592  3.030492965
## [144,]  0.3482802045  5.110401099
## [145,]  1.2513054291  3.805190444
## [146,]  1.1712628110 -2.235557316
## [147,]  2.9517305276 -1.577270767
## [148,] -5.3263581342  3.197981934
## [149,]  1.2624809282 -2.553426369
## [150,] -4.3819606673 -1.853276067
## [151,]  1.4317248284 -2.312981531
## [152,] -2.7488523406 -1.473871965
## [153,]  0.2922051977 -0.502726576
## [154,]  2.3870816009 -2.812984745
## [155,] -0.7343235991 -1.719120970
## [156,] -1.1528102666 -2.565321750
## [157,] -0.0601639058  4.673738922
## [158,]  0.1146378355  4.409006515
## [159,]  0.3515271973  3.884927151
## [160,] -1.3314963048  4.620142649
## [161,]  0.9673442554 -0.462674157
## [162,] -0.6333768183  4.540986782
## [163,] -0.4646259414  4.177195700
## [164,] -3.7902223684  1.407688914
## [165,] -0.3535215963  2.451973338
## [166,] 15.7307500564 -3.707477159
## [167,] -6.1688001102 -3.605725994
## [168,] -0.2323559640 -1.527023626
## [169,]  0.6435963454 -3.316657016
## [170,]  0.0242696839  4.169450209
## [171,] 15.8811831967 -2.587194128
## [172,]  0.1330703381  3.674169853
## [173,]  0.8227128901  3.850085260
## [174,] -1.5333074594 -1.336074055
## [175,]  0.0009126745 -1.313294653
## [176,] -0.2830442611 -0.005331427
## [177,] -2.0045304789  3.238047490
## [178,]  0.6362383927  4.299448015
## [179,] -0.0590189810  5.341830245
## [180,]  1.4963422105  4.003302221
## [181,]  0.8791660656 -1.512921601
## [182,] -1.8006217518 -2.631151539
## [183,]  2.4732671323 -4.602225818
## [184,]  1.4533627381 -3.835975431
## [185,]  1.8966622764 -4.674745760
## [186,]  1.9090020304 -4.939378606
## [187,]  2.2342528259 -4.963202840
## [188,]  2.5204518016 -4.841030992
## [189,]  0.9607437181 -3.542577283
## [190,] -3.7218827187 -6.213431612
## [191,] -2.1496463816 -3.845379384
## [192,]  1.1276274959 -4.392832778
## [193,]  1.4077236271 -3.453119117
## [194,]  1.6089292882 -5.094327594
## [195,]  2.1510350267 -5.380987515
## [196,]  2.6431189265 -4.363489739
## [197,]  2.0454251184 -4.412885986
## [198,]  2.2730642794 -4.313568663
## [199,]  1.5669859648 -4.435127984
## [200,]  0.9763873744 -0.223087521
## [201,] -0.9041903200  0.290263297
## [202,]  0.0683976704  0.609910835
## [203,] -0.0876707631 -0.365719678
## [204,] -0.4455327983  0.637346081
## [205,]  0.7089722511 -0.795760565
## [206,] -1.3747940318  0.187037447
## [207,] -0.9970786932 -1.021584408
## [208,] -0.2952073497 -0.308503493
## [209,]  0.4971538006  0.322187756
## [210,]  0.0488483929  0.244603407
## [211,] -0.4924412209  0.311312469
## [212,]  1.0463459058  4.362067410
## [213,] -0.1051974732  2.391490654
## [214,]  0.4095102276  4.212128307
## [215,]  0.8567573257  0.746863254
## [216,] -0.0231174642 -0.989582713
## [217,] -1.5484885294 -2.124600059
## [218,]  1.3651390568 -0.343987384
## [219,]  2.0454217727 -1.673124136
## [220,] -1.0721647731 -7.304154435
## [221,]  6.1390014960  1.742365003
## [222,] -1.2746745749 -2.030703617
## [223,]  0.8523333002 -2.183942681
## [224,]  3.5946345345 -2.939305828
## [225,] -1.9076033626 -0.551695412
## [226,] -0.3100745346 -1.146304092
## [227,]  0.9367146411 -1.791762320
## [228,]  2.5664750330 -1.948848454
## [229,]  4.2766093090 -1.252859878
## [230,] -1.4073458849 -0.359289650
## [231,]  0.0346369415 -1.871651278
## [232,] -0.9904137471 -1.954134757
## [233,]  0.8801871477 -2.971545542
## [234,] -1.7128411296 -3.116223644
## [235,]  0.7757586377 -1.166440659
## [236,] -3.1893104583 -6.583052642
## [237,] -7.7181835438 -1.849811056
## [238,] -2.9738006832 -2.497143776
## [239,]  0.7366906984 -2.522663753
## [240,] -4.8727494470 -1.492810559
## [241,] -1.6845135025 -3.679495017
## [242,]  1.7847597828  3.939927726
## [243,] -0.2847021701  3.780797226
## [244,]  0.0669335536  2.208060878
## [245,] -1.2527266239 -1.564688428
## [246,]  0.2659403489  1.435096079
## [247,]  0.1073706052  2.955113133
## [248,] -0.7698560166 -0.922443720
## [249,] -2.2083386044 -3.357628531
## [250,] -1.9465588735 -1.413884580
## [251,] -0.1839077675 -1.021514098
## [252,] -2.7623357883 -4.555463900
## [253,]  1.5223128603  4.709132963
## [254,]  3.5247908483 -2.356880745
## [255,] -2.4388490296 -0.248880416
## [256,]  0.3374642401 -1.668893155
## [257,]  0.3205690287  2.883236038
## [258,] -2.1608027522 -1.578842311
## [259,] -1.9476607158 -1.843573242
## [260,] -1.1587724673 -1.226317864
## [261,] -1.6766727964 -8.721810211
## [262,]  0.6856142901  4.806806422
## [263,] -0.7093028492 -1.407005840
## [264,] -2.3143877548 -0.772906146
## [265,] -2.4523653148 -0.928807244
## [266,] -1.5022588016 -1.503159813
## [267,]  3.5027698759 -3.437969237
## [268,]  1.1198638170  0.036429072
## [269,]  2.1139520248 -1.229743113
## [270,]  0.0833686859 -4.334733724
## [271,]  3.0262380534 -0.634698177
## [272,] -4.3718378779 -1.142981479
## [273,] -0.4896320601 -6.535422444
#Ugraph(P0.mRSG.6$sparseParCor, type = "fancy", lay = "layout_in_circle",
#       Vsize = 2, Vcex = .1, prune = T, cut = 0.5,
#       main = "miRNASeqGene data\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")
GGM.mRSG.6 = as.data.frame(GGMnetworkStats(P0.mRSG.6$sparseParCor, as.table = T))
GGM.mRSG.6.order = GGM.mRSG.6[order(GGM.mRSG.6$degree, decreasing = T), ]

#Output top 5%
GGM.mRSG.6.order[1:round(nrow(GGM.mRSG.6.order) * 0.05), ]
##                degree betweenness   closeness eigenCentrality nNeg nPos
## hsa-mir-206        74   1239.5984 0.002083333       1.0000000   38   36
## hsa-mir-137        72   2259.8181 0.002096436       0.9202624   39   33
## hsa-mir-1258       69   1835.3466 0.002049180       0.7615516   34   35
## hsa-mir-3923       65   1442.4219 0.001949318       0.6630538   36   29
## hsa-mir-135a-2     63    949.9530 0.001923077       0.7805614   33   30
## hsa-mir-656        61   1236.5778 0.001845018       0.9967304   14   47
## hsa-mir-216b       58   1350.7417 0.001984127       0.6549100   29   29
## hsa-mir-488        58   1184.0701 0.001992032       0.6766936   24   34
## hsa-mir-329-1      57    774.6184 0.001785714       0.9710800    9   48
## hsa-mir-668        56    787.2091 0.001811594       0.9837761    6   50
## hsa-mir-1197       55    564.0291 0.001757469       0.9666753    7   48
## hsa-mir-218-1      53   1037.2767 0.001845018       0.5402034   27   26
## hsa-mir-1185-1     52    343.5733 0.001647446       0.9043083    4   48
## hsa-mir-873        52   1304.6211 0.001953125       0.6201089   30   22
## hsa-mir-1185-2     51    283.4901 0.001666667       0.9273307    4   47
## hsa-mir-665        51   1297.0284 0.001912046       0.8758355   17   34
## hsa-mir-770        51    638.3712 0.001776199       0.9423813    5   46
## hsa-mir-1251       50    432.9843 0.001926782       0.6195067   16   34
## hsa-mir-380        50    460.8525 0.001766784       0.9156204    6   44
## hsa-mir-767        50    931.9401 0.001945525       0.5872044   23   27
## hsa-mir-3166       48    633.1112 0.001831502       0.5249175   26   22
## hsa-mir-376a-2     48    442.7223 0.001760563       0.9137662    4   44
## hsa-mir-31         47    477.2020 0.001848429       0.5364720   29   18
## hsa-mir-383        47    691.4625 0.001908397       0.5459420   27   20
##                mutualInfo variance partialVar
## hsa-mir-206    0.03647879 1.037152          1
## hsa-mir-137    0.03038218 1.030848          1
## hsa-mir-1258   0.02875697 1.029174          1
## hsa-mir-3923   0.02582764 1.026164          1
## hsa-mir-135a-2 0.02928413 1.029717          1
## hsa-mir-656    0.01769005 1.017847          1
## hsa-mir-216b   0.02227444 1.022524          1
## hsa-mir-488    0.01846425 1.018636          1
## hsa-mir-329-1  0.02054543 1.020758          1
## hsa-mir-668    0.01728309 1.017433          1
## hsa-mir-1197   0.01636555 1.016500          1
## hsa-mir-218-1  0.02035512 1.020564          1
## hsa-mir-1185-1 0.01356332 1.013656          1
## hsa-mir-873    0.02388268 1.024170          1
## hsa-mir-1185-2 0.01283176 1.012914          1
## hsa-mir-665    0.01249051 1.012569          1
## hsa-mir-770    0.01311216 1.013198          1
## hsa-mir-1251   0.02468907 1.024996          1
## hsa-mir-380    0.01221634 1.012291          1
## hsa-mir-767    0.02432526 1.024624          1
## hsa-mir-3166   0.01694897 1.017093          1
## hsa-mir-376a-2 0.01033047 1.010384          1
## hsa-mir-31     0.01716165 1.017310          1
## hsa-mir-383    0.01957979 1.019773          1
ggplot(GGM.mRSG.6.order, aes(x = reorder(rownames(GGM.mRSG.6.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.mRSG.6.order$degree), linetype = "dashed", color = "red")  +
  # 24th unit: top 5%
  geom_hline(yintercept = GGM.mRSG.6.order[24,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "miRNASeqGene", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-6")

D. All data

set.seed(42)
opt.all = optPenalty.kCVauto(Y = data.numeric, lambdaMin = 1e-11, lambdaMax = 10)
#opt.all.10 = optPenalty.kCVauto(Y = data.numeric, lambdaMin = 1e-11, lambdaMax = 1000, fold = 10)
#opt.all.5 = optPenalty.kCVauto(Y = data.numeric, lambdaMin = 1e-11, lambdaMax = 1000, fold = 5)
#opt.all.3 = optPenalty.kCVauto(Y = data.numeric, lambdaMin = 1e-11, lambdaMax = 1000, fold = 3)

#setNames(c(3,5,10,43), c(opt.all.3$optLambda, opt.all.5$optLambda, opt.all.10$optLambda, opt.all$optLambda))
opt.all$optLambda
## [1] 1.401621
edgeHeat(opt.all$optPrec, diag = F, textsize = 0.1)
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

CNplot(covML(data.numeric),
       lambdaMin = 1e-11,
       lambdaMax = 1000,
       step = 5000,
       Iaids = T,
       vertical = T,
       value = opt.all$optLambda)
## Perform input checks... 
## Calculating spectral condition numbers... 
## Calculating interpretational aids... 
## Plotting...

i = 0.1
edgeHeat(M = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  46139 
## - Corresponding to 18.81 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.5
edgeHeat(M = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  46110 
## - Corresponding to 18.79 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.9
edgeHeat(M = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  32121 
## - Corresponding to 13.09 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 0.999
edgeHeat(M = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  18492 
## - Corresponding to 7.54 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 1-1e-6
edgeHeat(M = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  11370 
## - Corresponding to 4.63 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

i = 1-1e-10
edgeHeat(M = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=i, verbose = F)$sparseParCor,
             diag = F, textsize = 1,
             main =  paste("False Discovery Rate cutoff:", i))
## - Retained elements:  7263 
## - Corresponding to 2.96 % of possible edges 
## 
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

Color mapping for different omics:

Smallest possible FDRcut:

P0.all.min = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=1-1e-14)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  5784 
## - Corresponding to 2.36 % of possible edges 
## 
PcorP = pruneMatrix(P0.all.min$sparseParCor)
Colors.min <- rownames(PcorP)
Colors.min[grep("hsa", rownames(PcorP))] <- "red"
Colors.min[grep(".RPPA", rownames(PcorP))] <- "green"
Colors.min[grep(".R2Gn", rownames(PcorP))] <- "cyan"
#dev.new(width = 20, height = 20, unit = "in", noRStudioGD = F)
set.seed(42)
Ugraph(PcorP, type = "fancy", lay = "layout_with_fr", 
       Vcolor = Colors.min, Vsize = 2, Vcex = .3, prune = T, cut = 0.5,
       main = "All Numerical data\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\nRed: miRNASeqGene; Blue: RNASeq2GeneNorm; Green: RPPA Array")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##               [,1]        [,2]
##   [1,]  6.20561655  3.53495957
##   [2,]  7.46396525 -0.77381923
##   [3,]  7.09032175  6.23813463
##   [4,]  0.44980686 -0.71576151
##   [5,] -4.70019262 10.22128884
##   [6,] -1.69553119  3.25018459
##   [7,] -2.64191096  3.98895642
##   [8,] -4.04638984 11.18501863
##   [9,] -8.80321991 -0.93321937
##  [10,] -0.68545340  3.51833639
##  [11,] -5.04246353  5.15978586
##  [12,] -4.15680078  4.73729267
##  [13,] -2.87979060  4.27410950
##  [14,] -4.95681191  4.43927019
##  [15,] -1.55025292  3.31852244
##  [16,] -3.70730696  2.73799578
##  [17,]  2.92346834  4.32803403
##  [18,]  0.11476041  1.27095399
##  [19,]  5.18051584  9.17247255
##  [20,] -0.97149384  3.71746115
##  [21,] -1.65655542  2.69010492
##  [22,] -0.75016099  4.02487543
##  [23,] -1.32427219  3.74061979
##  [24,] -0.46707460  4.63068239
##  [25,] -2.22970578  6.59677246
##  [26,]  1.44494776  2.14949872
##  [27,] -0.44455543  3.43419864
##  [28,] -3.59201386  6.28705967
##  [29,]  0.86161343  4.00550641
##  [30,]  1.52064344  2.97018628
##  [31,]  6.12123851  4.16387269
##  [32,]  4.32099628  5.28189663
##  [33,] -2.78424857  2.59963354
##  [34,] -6.74102748 -3.39571616
##  [35,] -0.70437176  2.98939932
##  [36,] -9.72616996  7.84655216
##  [37,] -0.47334387  4.25685297
##  [38,] -1.02886612  4.79243406
##  [39,]  0.76019974  0.80779110
##  [40,] -2.71461657  1.53766897
##  [41,] -1.40784584 -3.50587806
##  [42,] -2.25151573  4.34172313
##  [43,] -1.53288446  4.04875446
##  [44,] -0.33762429 -1.04753151
##  [45,]  0.18858460  4.46202711
##  [46,] -0.37527163  1.43662306
##  [47,] -2.27722848  3.26684735
##  [48,] -0.04136483  3.22492507
##  [49,] -2.34062048 -2.17611840
##  [50,] -2.21332439  3.73746074
##  [51,] -6.09103371  7.75167631
##  [52,]  0.71016522  3.37764081
##  [53,] -1.14186944  3.58850216
##  [54,]  0.45387535  1.59998812
##  [55,] -8.67958568  7.34194436
##  [56,] -1.10736393  3.43993741
##  [57,] -0.92100147  4.50166466
##  [58,] -0.14741028  5.49378117
##  [59,] -4.01177196  1.30019931
##  [60,] -1.79203421 -0.96592241
##  [61,]  1.96749301  8.20237105
##  [62,] -6.11672901  0.41633904
##  [63,]  8.25274443  1.11661797
##  [64,] -8.15952840  1.13732458
##  [65,]  0.84800368 -3.12260948
##  [66,] -4.69365442 -1.94822063
##  [67,] -0.81929430  3.51094873
##  [68,]  0.16582338  4.28885181
##  [69,] -5.66803404  7.45634578
##  [70,]  3.29135542  6.54025057
##  [71,]  4.17232889  7.40701842
##  [72,]  3.77524161  6.24468401
##  [73,]  0.64816384  6.83271701
##  [74,]  1.35992752  5.08253069
##  [75,] -0.05546907  3.46988660
##  [76,] -1.20478188  2.77475443
##  [77,]  0.82965158 11.46212178
##  [78,] -0.80945978  2.18626332
##  [79,] -1.92020438  3.49318649
##  [80,] -2.92497916 -1.39592158
##  [81,]  7.13704693 -1.19015440
##  [82,] -5.45639431 -0.21282837
##  [83,] -5.73172816  0.19168379
##  [84,] -6.47416503  1.98061445
##  [85,] -0.27757270  5.91410184
##  [86,]  0.08391602  5.07119998
##  [87,]  1.36971015  8.39508663
##  [88,]  0.69276474 -1.51504462
##  [89,] -1.13335456 -1.60402484
##  [90,] -1.58819452 -1.54408671
##  [91,] -0.45600533  2.21497976
##  [92,]  0.28221268  2.30177437
##  [93,]  1.84662476  6.11775540
##  [94,]  1.27101054  1.15768912
##  [95,]  1.48752823  3.72754132
##  [96,] -2.11491601  4.69886612
##  [97,] -1.21368886  3.99295432
##  [98,] -3.71256434 11.64260968
##  [99,] -2.21892849  0.81004706
## [100,] -0.67735206  1.84965454
## [101,]  1.33259548 -0.58351819
## [102,]  0.01784759  8.94833166
## [103,]  0.28782953  4.07208264
## [104,] -1.42034627  2.91075199
## [105,]  0.05537023  6.07065205
## [106,] -0.62928824  4.19030920
## [107,] -1.27972908  9.82121821
## [108,] -4.50077273  1.60140635
## [109,]  2.74955122 -4.70948408
## [110,]  4.39729356 -1.53832635
## [111,]  2.56254112 -1.17832362
## [112,] -0.26962669  0.56403246
## [113,] -3.89496138 -1.00521823
## [114,] -7.86685783 -2.39844328
## [115,]  1.22738117  4.84513175
## [116,] -0.36397592  3.35443251
## [117,] -7.39373042  6.38622879
## [118,]  1.91092406 -2.84625309
## [119,]  2.07654585  2.27034811
## [120,] -2.10709699  9.84152160
## [121,]  8.12804100  2.33295620
## [122,] -1.66603773  6.34373992
## [123,] -0.52473197  3.14476425
## [124,] -3.31712891  3.59800862
## [125,] -1.68487396  2.96610667
## [126,]  0.55021039  3.64030411
## [127,] -2.91485909  6.29250924
## [128,] -1.24447093  2.12825541
## [129,] -0.81354316  2.63284498
## [130,] -0.15815404  3.69109849
## [131,]  0.29257953  2.44257024
## [132,] -1.49354354  3.53231106
## [133,] -0.32755883  4.65494480
## [134,] -1.02953258  3.94697401
## [135,] -5.89855918 -0.91810076
## [136,] -1.48454464  1.51235982
## [137,] -1.76841174  3.65942250
## [138,] -6.53175790  5.82577306
## [139,] -6.68988019  5.31456457
## [140,] -2.92027793 -5.38247340
## [141,] -4.87503088  5.06762417
## [142,] -3.52520912  5.12297931
## [143,]  1.96331166 -4.92802211
## [144,]  2.22022445  0.13738210
## [145,]  3.96200014 -3.44267559
## [146,]  4.07624974 -0.70359886
## [147,]  3.25245646 10.89986377
## [148,] -0.63492513  4.36275168
## [149,] -5.06756978 -4.35156183
## [150,] -1.12057451  3.14822853
## [151,] -2.03905255  0.81878143
## [152,]  7.65706014  0.79983728
## [153,] -0.11971404  4.62508311
## [154,]  0.99751454  3.84464132
## [155,] -1.95827056  3.23656576
## [156,]  0.02069920 -4.73951677
## [157,] -0.19124571  1.61117144
## [158,] -2.75561679 11.95290923
## [159,]  2.55843478 11.17143487
## [160,] -1.18816672  6.60500034
## [161,]  6.88889153 -1.68369652
## [162,] -3.48737316  2.38682102
## [163,] -0.67405184  4.99401868
## [164,]  0.52099160  3.33873255
## [165,] -0.87176412  3.21692198
## [166,] -1.02275956  1.49541339
## [167,]  4.95275986  6.83440732
## [168,] -0.46107902  1.68856694
## [169,] -1.50130335  3.89449675
## [170,] -1.78068450  4.55133529
## [171,] -1.08104114  3.77046568
## [172,] -7.47547085  6.80962106
## [173,] -0.51648103  3.75850951
## [174,] -2.43808599  7.29156802
## [175,] -4.64256281  5.93382600
## [176,] -3.98890057  5.77674920
## [177,] -5.74821097  6.78257838
## [178,] -8.88539196  6.06565213
## [179,] -7.14895947  7.36902307
## [180,] -8.26745356  7.57307701
## [181,] -3.79006129  4.47767523
## [182,] -9.57019364  5.39507434
## [183,] -7.94516592  5.99942927
## [184,]  0.63965551  3.00609291
## [185,]  1.45836826  4.19857026
## [186,] -5.77838508  3.71493154
## [187,]  0.86679640  3.61779292
## [188,]  0.28620957  1.42585685
## [189,] -0.30089294  2.27894193
## [190,] -0.58394334  5.25250982
## [191,] -2.05680690  3.05772390
## [192,] -0.74836461  0.87331356
## [193,] -1.04770126  3.29799509
## [194,] -1.38754245  4.49881412
## [195,] -8.07797408  5.67146411
## [196,] -9.10710607  6.71788492
## [197,] -4.86992509  4.84669090
## [198,] -2.98942007  7.21140005
## [199,] -0.12842693  3.08713744
## [200,] -9.05879044  4.84162142
## [201,] -7.63813416  4.98524203
## [202,]  0.62309959  5.70389132
## [203,] -4.56756301  3.98431629
## [204,]  1.76789899  7.03393683
## [205,]  2.16852000  1.14831424
## [206,]  4.37514582  0.84933548
## [207,] -0.00686493  2.30267431
## [208,] -0.08301591 -0.22895130
## [209,] -6.10776391  5.60590145
## [210,]  2.68244847  6.57161777
## [211,] -5.32434117  5.36909945
## [212,] -8.46768648  5.32352573
## [213,] -1.03057404  1.98573349
## [214,] -0.46394987  2.86575064
## [215,] -1.80564666  2.91432837
## [216,] -3.29854269 11.42820994
## [217,] -6.01720742  6.73101284
## [218,] -6.52262908  6.59255872
## [219,] -7.26558925  5.85431101
## [220,] -6.10131551  1.00718786
## [221,] -0.32395253  2.98993790
## [222,]  4.54544200 -4.08831537
## [223,] -0.14236999  2.98885522
## [224,]  4.41351756  2.30248268
## [225,]  3.12004280  2.54982948
## [226,]  4.83282086  2.28120912
## [227,]  4.37508421  3.40924147
## [228,]  4.98494167  3.32432965
## [229,]  4.40479794  3.13689408
## [230,]  2.32997633  2.95252405
## [231,] -2.68327495  4.96229721
## [232,] -2.78332094  2.93622473
## [233,]  4.80429790  2.06169208
## [234,]  3.03770493  3.14275145
## [235,]  4.12523074  2.89486942
## [236,]  3.99211407  2.71085390
## [237,]  4.90576048  2.87143913
## [238,]  4.75665392  2.59693051
## [239,]  5.09489225  2.53111317
## [240,]  2.90290309  2.24706154
## [241,] -1.39056058  5.27036469
## [242,] -2.39333693  4.83307841
## [243,] -1.78007027  5.44595654
## [244,] -2.04067794  4.47520263
## [245,] -2.44908005  3.77798543
## [246,] -1.72440317  3.98964368
## [247,] -2.69972813  3.44874267
## [248,] -1.89773925  4.16021642
## [249,] -2.30312565  4.04402584
## [250,] -1.66864414  4.91588128
## [251,] -2.23812383  5.23486172
## [252,] -2.42246868  3.39355419
## [253,] -8.11629188  8.00193357
## [254,] -4.18698912  4.37672528
## [255,] -6.96364312  4.36592353
## [256,] -3.10117633  2.21506322
## [257,] -1.38133268  2.44111771
## [258,] -2.07222094  2.90083341
## [259,]  0.20942769  3.72371878
## [260,]  0.42267309  2.56949342
## [261,]  0.68542755  0.57104519
## [262,]  0.32303029  7.18250415
## [263,]  0.31399662  4.28757958
## [264,] -0.23429558  1.96143948
## [265,] -0.97963475  0.59609182
## [266,] -1.67032858  4.72927220
## [267,] -1.07203315  3.00440947
## [268,] -1.85946260  3.72898413
## [269,] -2.12940278  2.65169819
## [270,] -0.85829644 11.48930842
## [271,]  3.64627642  0.80176243
## [272,]  0.55892951  2.85850817
## [273,] -0.42248246  2.47514078
## [274,] -8.35398741 -1.35875741
## [275,]  0.81389733  2.95464074
## [276,] -1.85467744  5.19632064
## [277,] -1.01760059  5.14907153
## [278,]  7.94475226  1.63802249
## [279,] -1.58856148  2.60229414
## [280,] -1.27171356  7.81777573
## [281,] -3.32683692  0.92427556
## [282,] -2.80656328  1.89441055
## [283,] -0.56895976  2.40907603
## [284,]  3.15896755  3.85265613
## [285,]  4.37680292 -0.22288044
## [286,]  1.69401769  0.02003265
## [287,] -9.97130056  5.07490730
## [288,] -4.62275472  5.52954151
## [289,] -4.14367718  4.86283279
## [290,]  2.45290178  7.02223254
## [291,] -0.60374247  3.91470565
## [292,] -2.89995860  3.78073677
## [293,] -5.87847858  5.87245403
## [294,] -1.59324667  2.77996324
## [295,]  1.09239609  4.27343325
## [296,]  6.39339991  7.65771171
## [297,] -0.21934091  4.97111070
## [298,] -1.87781270  3.84751410
## [299,] -0.50266528  7.12800848
## [300,] -9.32555036  6.16933035
## [301,]  2.55110473  4.01127715
## [302,] -1.42405251  1.22084769
## [303,] -1.67332390  2.52529994
## [304,] -4.23481264  5.26258904
## [305,] -0.30793698  2.76331784
## [306,] -1.30070494  3.08582943
## [307,]  0.69487598  2.51061146
## [308,] -8.23802889  6.51520708
## [309,]  0.23591206  3.56078669
## [310,] -2.48410155  2.28279555
## [311,] -2.18111011  1.72028936
## [312,] -1.82942341  3.15321659
## [313,] -4.61445378 -0.96070591
## [314,]  0.10632790  4.88335179
## [315,]  0.64893666  5.30016925
## [316,] -2.35426115  4.57216559
## [317,] -2.33016615 -0.02105687
## [318,]  2.78645361  4.95745157
## [319,]  0.80979082  5.78119040
## [320,] -4.65047428 11.14237649
## [321,]  6.08945968  8.44513806
## [322,]  0.05956105  5.48266403
## [323,] -5.99730207  9.77777280
## [324,] -3.84385390 -5.23663395
## [325,] -1.20943724  1.68834238
## [326,] -1.39308904  6.69810330
## [327,] -2.39639507  2.11093029
## [328,]  0.71953099 -2.53750307
## [329,]  2.57131357  5.36262313
## [330,] -0.31198605  2.60744835
## [331,] -2.00511738 11.48052628
## [332,]  1.21210493  2.40284763
## [333,] -2.54334343 11.62641056
## [334,]  3.46626655  1.75273838
## [335,]  0.13190299  0.35810466
## [336,]  1.75877315  3.86709268
## [337,] -0.63730535  2.06664375
## [338,]  2.92051697  8.75314267
## [339,] -1.64100901 11.99736961
## [340,]  1.77019087 -1.75221361
## [341,]  3.23339752 -3.88754007
## [342,] -0.88451760  2.93985089
## [343,]  8.22042920  2.93564407
## [344,] -1.81016118  1.60571115
## [345,] -1.70358983  1.30340325
## [346,] -4.51590038 -4.55114956
## [347,]  6.77430135  7.02516237
## [348,]  0.54516226  2.17926622
## [349,] -0.60504632  8.28798181
## [350,] -0.89214700 -3.59384484
## [351,] -0.03332699  0.94730345
## [352,]  1.85872448 -1.21349062
## [353,]  2.13936616  6.90786003
## [354,] -1.89483400  2.13065897
## [355,] -4.31727250 -2.37203621
## [356,] -0.36165657 -1.84614881
## [357,] -1.40116424  2.30852471
## [358,] -0.18550006  3.85248935
## [359,] -0.33201224  3.54838705
## [360,]  1.33776450  1.86143279
## [361,] -0.06619153  9.91842326
## [362,]  7.99662562  0.28101230
## [363,] -4.23244482 -4.91018121
## [364,] -7.82481191  0.30721112
## [365,] -1.51151126  4.25331434
## [366,] -4.96156720  0.87470597
## [367,] -3.01617025  9.43661410
## [368,] -1.09208771  4.45073566
## [369,] -2.19932413 -5.40219844
## [370,] -4.48704678  0.08329512
## [371,] -1.84835264  2.36653659
## [372,] -3.30131600  7.03549437
## [373,]  0.91824536  1.98722453
## [374,] -2.58685199  0.51392047
## [375,] -3.18426135  3.25772822
## [376,]  0.68142328  1.39066331
## [377,]  5.64650382  4.93368254
## [378,] -0.04078212 -2.90411068
## [379,]  0.10624134 11.76048715
## [380,] -7.25368594 -3.00435347
## [381,]  2.68194363  5.80955822
## [382,] -0.93519288  4.14828233
## [383,] -0.39328304 -3.59359121
## [384,]  2.15193313  4.63930050
## [385,]  6.43825006 -2.17160348
## [386,]  3.72537705  3.87980276
## [387,] -5.07890901  2.68169578
## [388,] -8.07444656 -1.85921244
## [389,] -1.32498743  0.55494688
## [390,] -1.69525728 -6.31905719
## [391,]  7.72487303 -0.30683679
## [392,] -2.95401950 -3.29902366
## [393,]  0.37660040  2.88837231
## [394,]  3.91147731 -1.93426288
## [395,] -0.02146038  2.79282044
## [396,] -0.73827285  6.20082507
## [397,] -1.56594955 -0.04576967
## [398,]  0.37749086  0.65886951
## [399,] -1.36951151  1.44099775
## [400,]  3.05082991  7.17133393
## [401,]  4.67518205  9.15470102
## [402,] -6.40551642  2.80191459
## [403,] -1.91116751  8.59599960
## [404,] -1.29466983  2.62424564
## [405,]  3.60078611  7.96096064
## [406,]  0.59397540  8.62179331
## [407,] -2.07140509  2.11509594
## [408,]  4.87159108  1.49362716
## [409,]  4.82469197  0.18470023
## [410,]  4.23844402  5.83645365
## [411,]  0.33046295  3.03360648
## [412,]  2.59031249  0.63191672
## [413,] -0.90346526  2.38764242
## [414,] -0.31049383  0.88737480
## [415,]  0.65904252  1.95356224
## [416,]  0.61575787  3.90702182
## [417,]  0.23519819  1.95645750
## [418,] -0.09040095  4.44712331
## [419,]  1.62094376  1.46325598
## [420,] -0.56941003  1.30065917
## [421,]  0.49596715  4.02943567
## [422,] -2.99074837  0.12210378
## [423,] -0.24763669  6.30853611
## [424,] -7.42294030 -2.52197499
## [425,] -5.30367942  9.98833039
## [426,] -3.29289825 -5.03287788
## [427,] -4.96571016  3.15154371
## [428,]  8.15368512  3.63088290
## [429,]  2.79393831  0.96075594
## [430,] -3.51575244  0.35444461
## [431,]  3.81983104 10.40346165
## [432,]  0.75751663  9.70151101
## [433,] -6.33708113  1.61152048
## [434,]  1.43995254  7.60748016
## [435,]  5.29342087  6.02388355
## [436,] -0.68369244  2.43507974
## [437,] -2.40195711  2.03249615
## [438,] -0.82302575  4.53879504
## [439,]  0.19154348  4.63179146
## [440,]  0.41728056  3.39246980
## [441,] -0.08647039  2.44156408
## [442,]  1.17930327  2.91902559
## [443,] -1.07675347  2.47837536
## [444,] -1.18709554  3.08823029
## [445,]  1.40511284  6.01783596
## [446,] -1.07408906  2.25362674
## [447,] -1.36054138  4.10717284
## [448,]  0.44031066  4.95328368
## [449,]  3.12935649  5.16850366
## [450,]  0.85857824  4.83202108
## [451,] -0.60857871  2.69217744
## [452,]  0.15009231  3.89576006
## [453,] -1.34191076  4.54161755
## [454,]  1.78866983  4.26833853
## [455,] -1.00054721  8.77091947
## [456,] -1.02547450  1.17528365
## [457,]  0.97855207  3.06567513
## [458,]  0.21323945  2.76290086
## [459,]  1.08442612  4.52403479
## [460,]  2.02878208  3.51025782
## [461,]  0.02901680  1.81891696
## [462,] -0.22747103  4.01884055
## [463,]  0.29185595  3.83467755
## [464,] -0.80720618  1.54024221
## [465,]  1.94258479  2.90714312
## [466,] -0.10612897  3.32628155
## [467,] -1.25094261  4.84707182
## [468,]  1.09555320  3.27590602
## [469,] -1.63532742  2.00002325
#Ugraph(P0.all.min$sparseParCor, type = "fancy", lay = "layout_in_circle", 
#       Vcolor = Colors, Vsize = 2, Vcex = .1, prune = T, cut = 0.5,
#       main = "All Numerical data\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\nRed: miRNASeqGene; Blue: RNASeq2GeneNorm; Green: RPPA Array")
GGM.all.min = as.data.frame(GGMnetworkStats(P0.all.min$sparseParCor, as.table = T))
GGM.all.min.order = GGM.all.min[order(GGM.all.min$degree, decreasing = T), ]

#Output top 5%
GGM.all.min.order[1:round(nrow(GGM.all.min.order) * 0.05), ]
##                degree betweenness   closeness eigenCentrality nNeg nPos
## TFRC.RPPA         169  10373.5624 0.001277139       0.9850321   85   84
## G6PD.RPPA         159   9892.9879 0.001267427       0.9602916   82   77
## FN1.R2Gn          152   8133.2726 0.001206273       0.8971745   75   77
## PDCD4.RPPA        152   4317.4734 0.001215067       1.0000000   79   73
## GATA3.RPPA        149   7265.6529 0.001209190       0.8861581   82   67
## MYH11.RPPA        147   6234.8141 0.001236094       0.9386265   78   69
## GAPDH.R2Gn        145   7946.8327 0.001236094       0.8858551   90   55
## FASN.RPPA         139   4727.9854 0.001179245       0.8717441   67   72
## GAPDH.RPPA        136   6960.5542 0.001215067       0.8157612   76   60
## IGFBP2.RPPA       134   4194.5097 0.001219512       0.9011127   71   63
## HSPA1A.R2Gn       123   4596.7037 0.001175088       0.7832807   60   63
## SQSTM1.R2Gn       122   2717.4029 0.001187648       0.8582847   66   56
## ATM.RPPA          120   3611.8223 0.001183432       0.8328589   52   68
## EEF2.R2Gn         119   3132.9558 0.001201923       0.8634166   66   53
## SYP.R2Gn          108   2689.7774 0.001157407       0.7504667   62   46
## RPS6.R2Gn          92   2431.7576 0.001154734       0.6364864   50   42
## TGM2.R2Gn          86   1098.2465 0.001136364       0.6710201   45   41
## hsa-mir-206        80    784.1257 0.001090513       0.6025077   38   42
## PTEN.RPPA          79   1765.0714 0.001107420       0.5871044   44   35
## hsa-mir-135a-2     75    593.4168 0.001090513       0.5701744   39   36
## SERPINE1.R2Gn      75    615.6018 0.001095290       0.5987406   39   36
## MSH6.RPPA          74   1229.5074 0.001135074       0.6293040   40   34
## CTNNB1.R2Gn        73   1330.8679 0.001127396       0.5619557   37   36
## hsa-mir-137        72    500.2896 0.001088139       0.5893915   42   30
## hsa-mir-383        72   1269.5811 0.001097695       0.5421469   40   32
## hsa-mir-122        71    891.7021 0.001057082       0.5096415   42   29
## hsa-mir-577        71    605.3406 0.001113586       0.5741710   36   35
## RBM15.RPPA         67    635.4458 0.001068376       0.5442953   35   32
## EGFR.RPPA          67    531.9742 0.001081081       0.5499040   38   29
## TTF1.RPPA          67   1195.4745 0.001090513       0.4829827   37   30
## hsa-mir-216b       62    444.1900 0.001042753       0.5044072   35   27
## COL6A1.R2Gn        62    456.3206 0.001086957       0.5241064   32   30
## hsa-mir-329-1      61   3491.2505 0.001052632       0.2702204   16   45
## hsa-mir-34b        61    282.2201 0.001075269       0.5389143   35   26
## IGFBP2.R2Gn        61    396.6226 0.001064963       0.5108260   35   26
##                mutualInfo variance partialVar
## TFRC.RPPA      0.25183992 1.286390          1
## G6PD.RPPA      0.18495319 1.203162          1
## FN1.R2Gn       0.16828121 1.183269          1
## PDCD4.RPPA     0.18488663 1.203082          1
## GATA3.RPPA     0.17643277 1.192954          1
## MYH11.RPPA     0.21977181 1.245792          1
## GAPDH.R2Gn     0.17838389 1.195284          1
## FASN.RPPA      0.13972037 1.149952          1
## GAPDH.RPPA     0.23505358 1.264977          1
## IGFBP2.RPPA    0.15340686 1.165799          1
## HSPA1A.R2Gn    0.12917152 1.137885          1
## SQSTM1.R2Gn    0.12038576 1.127932          1
## ATM.RPPA       0.13056338 1.139470          1
## EEF2.R2Gn      0.13322568 1.142508          1
## SYP.R2Gn       0.09357269 1.098090          1
## RPS6.R2Gn      0.08136276 1.084764          1
## TGM2.R2Gn      0.06537766 1.067562          1
## hsa-mir-206    0.05378838 1.055261          1
## PTEN.RPPA      0.05664719 1.058282          1
## hsa-mir-135a-2 0.05338992 1.054841          1
## SERPINE1.R2Gn  0.05071932 1.052028          1
## MSH6.RPPA      0.06011756 1.061961          1
## CTNNB1.R2Gn    0.05987052 1.061699          1
## hsa-mir-137    0.04712975 1.048258          1
## hsa-mir-383    0.05167955 1.053038          1
## hsa-mir-122    0.04655721 1.047658          1
## hsa-mir-577    0.03825078 1.038992          1
## RBM15.RPPA     0.04954582 1.050794          1
## EGFR.RPPA      0.04636673 1.047458          1
## TTF1.RPPA      0.04556085 1.046615          1
## hsa-mir-216b   0.04401936 1.045003          1
## COL6A1.R2Gn    0.04746232 1.048607          1
## hsa-mir-329-1  0.02783208 1.028223          1
## hsa-mir-34b    0.03341964 1.033984          1
## IGFBP2.R2Gn    0.06572742 1.067936          1
Colors.min.plot <- rownames(GGM.all.min.order)
Colors.min.plot[grep("hsa", rownames(GGM.all.min.order))] <- "miRNASeqGene"
Colors.min.plot[grep(".RPPA", rownames(GGM.all.min.order))] <- "RPPA Array"
Colors.min.plot[grep(".R2Gn", rownames(GGM.all.min.order))] <- "RNASeq2GeneNorm"
ggplot(GGM.all.min.order, aes(x = reorder(rownames(GGM.all.min.order), -degree), y = degree, color = Colors.min.plot)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.all.min.order$degree), linetype = "dashed", color = "red")  +
  # 36th unit: top 5%
  geom_hline(yintercept = GGM.all.min.order[36,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "All Data", guide = guide_axis(angle = 90)) +
  scale_color_manual(values = c("red", "blue", "green3"))+
  ggtitle("Variables sorted by degree, FDR = 1-1e-14")

FDRcut 1-1e-6:

P0.all.6 = sparsify(opt.all$optPrec, threshold = "localFDR", FDRcut=1-1e-6)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  11370 
## - Corresponding to 4.63 % of possible edges 
## 
PcorP.6 = pruneMatrix(P0.all.6$sparseParCor)
Colors.6 <- rownames(PcorP.6)
Colors.6[grep("hsa", rownames(PcorP.6))] <- "red"
Colors.6[grep(".RPPA", rownames(PcorP.6))] <- "green"
Colors.6[grep(".R2Gn", rownames(PcorP.6))] <- "cyan"
#dev.new(width = 20, height = 20, unit = "in", noRStudioGD = F)
set.seed(42)
Ugraph(PcorP.6, type = "fancy", lay = "layout_with_fr", 
       Vcolor = Colors.6, Vsize = 2, Vcex = .3, prune = T, cut = 0.5,
       main = "All Numerical data\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\nRed: miRNASeqGene; Blue: RNASeq2GeneNorm; Green: RPPA Array")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##               [,1]         [,2]
##   [1,] -1.60759887 -6.971002543
##   [2,] -0.63663163  3.406811549
##   [3,]  9.65375258  3.702930524
##   [4,]  9.10446249 -0.011686856
##   [5,]  9.79885530  7.024422253
##   [6,] -5.87970713  3.807967700
##   [7,]  3.83399865 -3.805636798
##   [8,]  1.42817854 -1.897861225
##   [9,]  9.44455630  7.452278140
##  [10,]  2.21521272  1.071936169
##  [11,]  3.21657003  1.553532256
##  [12,] 10.00105487  0.123812260
##  [13,]  3.13631927 -7.908435787
##  [14,] -0.60331983  6.050929837
##  [15,]  2.90717255  0.904118360
##  [16,]  4.39441511  3.302756223
##  [17,]  4.53911660  3.183406504
##  [18,]  4.50487045  2.856305398
##  [19,]  4.14870195  2.988487473
##  [20,]  2.80412156  0.564845063
##  [21,]  4.65702236 -0.607470487
##  [22,]  0.14921829  1.593027528
##  [23,]  2.20221006  2.477653252
##  [24,] -0.99358917  1.185969190
##  [25,]  3.45328558  0.461906670
##  [26,]  3.32356930  0.812676144
##  [27,]  2.94207072 -0.004678972
##  [28,]  3.00265127  1.412332018
##  [29,]  3.78418141  0.947696248
##  [30,]  5.16723410  1.427714147
##  [31,]  1.41965427  2.419024142
##  [32,]  3.68534379  1.330132944
##  [33,]  3.08994322  3.685021838
##  [34,]  2.81001031 -0.320208247
##  [35,]  1.82260488  0.127538867
##  [36,] -0.78964152  0.566770751
##  [37,]  0.20328025  2.032256302
##  [38,]  1.43659164  1.937828010
##  [39,]  6.33046621  2.033043091
##  [40,]  1.97653169  0.948642046
##  [41,]  5.82921334  4.407123391
##  [42,]  2.22102859  1.690463845
##  [43,]  3.18413060  2.098229024
##  [44,]  1.53187020 -0.589330033
##  [45,]  4.64792697  0.963846724
##  [46,] 12.16630141  1.849875188
##  [47,]  3.43590952 -2.171149748
##  [48,]  2.73059462  2.262297867
##  [49,]  3.51395173  1.357800987
##  [50,] -0.21676758  0.297461811
##  [51,]  3.53132612  1.548096779
##  [52,]  3.08259394 -0.302345494
##  [53,]  3.65007983 -8.350629198
##  [54,]  3.09762208  0.535146143
##  [55,]  3.11725764  0.657009947
##  [56,] -3.29674553  2.977827292
##  [57,]  3.53641162  0.273707188
##  [58,]  6.37607308  3.990660498
##  [59,]  3.32848656  2.092907999
##  [60,]  3.14493341  1.099299379
##  [61,]  2.70671353 -0.071397769
##  [62,]  4.80585467  4.931344584
##  [63,]  3.26550179  0.673328951
##  [64,]  2.52525051  2.140143958
##  [65,]  3.24776762  2.392186404
##  [66,]  0.89898101  0.790033551
##  [67,]  3.66673568 -1.394558704
##  [68,] -2.64261289  4.009837366
##  [69,]  1.93970645 -2.465279646
##  [70,]  2.07011231 -8.321980621
##  [71,]  7.16641824  0.527015581
##  [72,]  5.84028207 -1.854570639
##  [73,] -3.14195608  1.736751438
##  [74,] -1.18498995 -4.757464224
##  [75,]  0.14565656  3.270983783
##  [76,]  6.27286924 -3.100545865
##  [77,]  7.27604411 -0.040546139
##  [78,]  2.98594810  1.768944449
##  [79,]  2.38088636 -0.126680669
##  [80,]  5.58629528  4.398411909
##  [81,]  1.83941319  5.044007665
##  [82,] -2.39339924 -6.827511255
##  [83,] -5.76604251 -2.356450845
##  [84,] -4.81695199 -4.007367383
##  [85,]  3.19548808 -2.956202450
##  [86,]  1.95832365 -2.908362353
##  [87,]  2.24422792 -1.531710487
##  [88,]  2.69194415 -1.199167427
##  [89,]  2.46097661  0.507824413
##  [90,]  3.22369670  1.555493749
##  [91,]  8.61713519 -2.066579175
##  [92,]  4.52468846 -6.246089360
##  [93,]  3.05919704  5.206068532
##  [94,]  2.40690107  1.596689805
##  [95,]  3.06584768  0.042861774
##  [96,] -0.77867019  0.125268947
##  [97,] 10.88328393  5.726342032
##  [98,]  1.11174271  7.077559492
##  [99,] -1.03189215 -1.119035545
## [100,] -1.14305288 -0.385196909
## [101,] -0.25221591  2.032464263
## [102,]  1.00391593 -0.372560178
## [103,]  1.90825752  0.333703950
## [104,] -0.40380582  1.788242317
## [105,] -0.34718507 -1.980829302
## [106,] -0.82061281 -1.294119225
## [107,] -1.42914381 -0.050385575
## [108,]  2.35011430  1.119444198
## [109,]  3.42494217 -0.007748603
## [110,]  8.11443800 -6.137747700
## [111,]  4.44707647 -1.514788840
## [112,]  2.64108579 -0.903531450
## [113,]  4.06096167 -0.569297806
## [114,]  4.01920439  1.083722908
## [115,]  3.59063609  1.208312716
## [116,] 11.64701518  3.122058570
## [117,] -6.21576085  3.204468540
## [118,]  4.37092835 -1.993102368
## [119,]  2.52368244 -0.291096098
## [120,]  0.48131310 -1.573011707
## [121,]  0.55061767 -2.038853410
## [122,]  2.63668149  1.567265450
## [123,]  2.14250208  0.806184950
## [124,]  1.07406679  2.101886515
## [125,]  2.52390297  0.269278270
## [126,]  4.38022811 -2.485257811
## [127,]  1.99535850 -2.043455734
## [128,] 10.84368167 -4.029261312
## [129,] -1.66092269 -2.909372494
## [130,] -1.66837111 -2.362266022
## [131,] -0.63584362 -4.081975101
## [132,]  4.81592924  1.540408067
## [133,]  0.12360468 -1.723852348
## [134,] -1.98339774 -4.131527688
## [135,] -2.21422659  8.811684986
## [136,]  5.17140901  0.882242535
## [137,] -0.85423057  9.518405893
## [138,] -0.27253626  9.658701366
## [139,]  2.31968761  1.808823218
## [140,]  5.92785782  4.903646091
## [141,] 10.12250099  6.602818642
## [142,]  4.15189861  7.555871306
## [143,] -1.64776871  5.677174846
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## [543,] -0.92204545  1.784282491
## [544,]  7.71054043 -2.709107943
## [545,]  7.68481025 -7.512589103
## [546,]  3.79147634 -1.952686696
## [547,]  0.71402317 -1.176184121
## [548,]  1.93759990 -0.052098310
## [549,]  7.34235730 -5.108250342
## [550,]  1.75504905  2.359294202
## [551,]  2.89157093 -0.089567419
## [552,]  2.71166168  0.410546464
## [553,]  2.49052976  1.186647061
## [554,]  1.58935541  0.255574363
## [555,]  2.14886733 -0.459854990
## [556,]  2.18161790  0.571441079
## [557,]  2.57445028  0.821699130
## [558,]  1.82283446 -1.263700536
## [559,]  2.33521847  0.960257037
## [560,]  4.26227302  1.827358285
## [561,]  2.06770111  1.524940029
## [562,]  0.77444939  2.069445951
## [563,]  1.17349417  0.786787144
## [564,]  2.05361694 -0.061176133
## [565,]  2.34932352 -0.187690050
## [566,]  2.51423592  0.900613052
## [567,]  1.45187967 -0.559614382
## [568,]  5.34204239 -2.170617564
## [569,]  4.01616536  1.230238755
## [570,]  1.50579556  0.631334897
## [571,]  2.97268746  0.213392334
## [572,]  1.19192657  1.236468642
## [573,]  0.75895826  0.931771759
## [574,]  3.87741644 -0.437571703
## [575,]  2.70270048  0.870789366
## [576,]  2.82770952 -0.543354164
## [577,]  1.63292827  1.235686118
## [578,]  1.14888167  1.855000346
## [579,]  2.24822391  1.330031173
## [580,]  4.41017885  1.323660713
## [581,]  2.41104480 -0.473188274
## [582,]  2.55090017  1.437489797
#Ugraph(P0.all.6$sparseParCor, type = "fancy", lay = "layout_in_circle", 
#       Vcolor = Colors, Vsize = 2, Vcex = .1, prune = T, cut = 0.5,
#       main = "All Numerical data\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\nRed: miRNASeqGene; Blue: RNASeq2GeneNorm; Green: RPPA Array")
GGM.all.6 = as.data.frame(GGMnetworkStats(P0.all.6$sparseParCor, as.table = T))
GGM.all.6.order = GGM.all.6[order(GGM.all.6$degree, decreasing = T), ]

#Output top 5%
GGM.all.6.order[1:round(nrow(GGM.all.6.order) * 0.05), ]
##                degree betweenness    closeness eigenCentrality nNeg nPos
## TFRC.RPPA         246  14797.5257 0.0010857763       1.0000000  132  114
## FN1.R2Gn          236  12443.8642 0.0010718114       0.9457201  131  105
## FASN.RPPA         231  12054.2369 0.0010526316       0.9783767  114  117
## G6PD.RPPA         220  11507.5629 0.0010438413       0.9260943  114  106
## GAPDH.R2Gn        219  13510.0325 0.0010515247       0.8886630  121   98
## MYH11.RPPA        217   8518.6384 0.0010438413       0.9830667  108  109
## GATA3.RPPA        216   8182.4011 0.0010384216       0.9091354  118   98
## GAPDH.RPPA        214  11170.7529 0.0010362694       0.8851330  108  106
## PDCD4.RPPA        213   6679.3839 0.0010416667       0.9797947  104  109
## IGFBP2.RPPA       205   7040.7845 0.0010373444       0.9389363  108   97
## SQSTM1.R2Gn       189   5469.4256 0.0010111223       0.8873399  102   87
## HSPA1A.R2Gn       184   4968.5134 0.0009891197       0.7945652   83  101
## EEF2.R2Gn         181   3159.9552 0.0010121457       0.8839068   93   88
## ATM.RPPA          172   3993.4665 0.0010020040       0.8264090   84   88
## SYP.R2Gn          156   2202.8859 0.0009661836       0.8222125   80   76
## RPS6.R2Gn         153   3690.7506 0.0009737098       0.7168802   81   72
## TGM2.R2Gn         141   1958.3442 0.0009606148       0.7160260   72   69
## PTEN.RPPA         137   2597.9479 0.0009328358       0.6853480   78   59
## hsa-mir-122       135   1554.4340 0.0009157509       0.6267499   67   68
## CTNNB1.R2Gn       127   2018.0639 0.0009496676       0.6685270   58   69
## MSH6.RPPA         127    820.9801 0.0009578544       0.7322693   72   55
## RBM15.RPPA        125   1396.8686 0.0009442871       0.6570886   67   58
## SERPINE1.R2Gn     122   1089.1721 0.0009319664       0.6360909   59   63
## TTF1.RPPA         122    953.8346 0.0009216590       0.6117119   60   62
## hsa-mir-383       121   1517.4066 0.0009250694       0.6220908   71   50
## hsa-mir-137       120   1086.1332 0.0009389671       0.6863619   65   55
## hsa-mir-135a-2    118    655.2413 0.0009372071       0.6643085   58   60
## IGFBP2.R2Gn       115   1043.2269 0.0009259259       0.6057483   67   48
## hsa-mir-206       113    609.7036 0.0009107468       0.6208832   53   60
## hsa-mir-3923      112    963.3938 0.0009407338       0.6335249   47   65
## hsa-mir-577       112    729.4975 0.0009174312       0.6026398   54   58
## hsa-mir-216b      111    698.0320 0.0008865248       0.5858131   61   50
## COL6A1.R2Gn       110   1091.9639 0.0009354537       0.6032470   57   53
## EGFR.RPPA         105    674.7148 0.0009259259       0.5969085   57   48
## hsa-mir-216a      104    661.3425 0.0008960573       0.5560394   53   51
##                mutualInfo variance partialVar
## TFRC.RPPA      0.25622527 1.292044          1
## FN1.R2Gn       0.17526080 1.191557          1
## FASN.RPPA      0.15241862 1.164648          1
## G6PD.RPPA      0.18646593 1.204984          1
## GAPDH.R2Gn     0.18534206 1.203630          1
## MYH11.RPPA     0.22591537 1.253470          1
## GATA3.RPPA     0.18283443 1.200616          1
## GAPDH.RPPA     0.24242772 1.274339          1
## PDCD4.RPPA     0.18953406 1.208686          1
## IGFBP2.RPPA    0.15876069 1.172057          1
## SQSTM1.R2Gn    0.12501668 1.133167          1
## HSPA1A.R2Gn    0.13556427 1.145183          1
## EEF2.R2Gn      0.13791771 1.147881          1
## ATM.RPPA       0.13585486 1.145516          1
## SYP.R2Gn       0.09556231 1.100277          1
## RPS6.R2Gn      0.08739767 1.091331          1
## TGM2.R2Gn      0.07119594 1.073792          1
## PTEN.RPPA      0.06466836 1.066805          1
## hsa-mir-122    0.05631111 1.057927          1
## CTNNB1.R2Gn    0.06430739 1.066420          1
## MSH6.RPPA      0.06546424 1.067655          1
## RBM15.RPPA     0.05650845 1.058136          1
## SERPINE1.R2Gn  0.05697306 1.058627          1
## TTF1.RPPA      0.05249557 1.053898          1
## hsa-mir-383    0.05710122 1.058763          1
## hsa-mir-137    0.05268007 1.054092          1
## hsa-mir-135a-2 0.05779898 1.059502          1
## IGFBP2.R2Gn    0.07063229 1.073187          1
## hsa-mir-206    0.05519122 1.056743          1
## hsa-mir-3923   0.04509524 1.046127          1
## hsa-mir-577    0.04391752 1.044896          1
## hsa-mir-216b   0.04931901 1.050555          1
## COL6A1.R2Gn    0.05208990 1.053470          1
## EGFR.RPPA      0.04995200 1.051221          1
## hsa-mir-216a   0.04801197 1.049183          1
Colors.6.plot <- rownames(GGM.all.6.order)
Colors.6.plot[grep("hsa", rownames(GGM.all.6.order))] <- "miRNASeqGene"
Colors.6.plot[grep(".RPPA", rownames(GGM.all.6.order))] <- "RPPA Array"
Colors.6.plot[grep(".R2Gn", rownames(GGM.all.6.order))] <- "RNASeq2GeneNorm"
ggplot(GGM.all.6.order, aes(x = reorder(rownames(GGM.all.6.order), -degree), y = degree, color = Colors.6.plot)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.all.6.order$degree), linetype = "dashed", color = "red")  +
  # 36th unit: top 5%
  geom_hline(yintercept = GGM.all.6.order[36,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "All Data", guide = guide_axis(angle = 90)) +
  scale_color_manual(values = c("red", "blue", "green3"))+
  ggtitle("Variables sorted by degree, FDR = 1-1e-6")

2. Split data into alive and dead

A. RPPAArray

data.RPPA.alive = data.RPPA[which(data.Y == 0), ]
data.RPPA.dead = data.RPPA[which(data.Y == 1), ]
set.seed(42)
opt.RPPA.alive = optPenalty.kCVauto(Y = data.RPPA.alive, lambdaMin = 1e-11, lambdaMax = 10)
opt.RPPA.dead = optPenalty.kCVauto(Y = data.RPPA.dead, lambdaMin = 1e-11, lambdaMax = 10)
opt.RPPA.alive$optLambda
## [1] 0.002760197
opt.RPPA.dead$optLambda
## [1] 1.07822
edgeHeat(opt.RPPA.alive$optPrec, diag = F, textsize = 7)
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

edgeHeat(opt.RPPA.dead$optPrec, diag = F, textsize = 7)
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

CNplot(covML(data.RPPA.alive),
       lambdaMin = 1e-11,
       lambdaMax = 1000,
       step = 5000,
       Iaids = T,
       vertical = T,
       value = opt.RPPA.alive$optLambda)
## Perform input checks... 
## Calculating spectral condition numbers... 
## Calculating interpretational aids... 
## Plotting...

CNplot(covML(data.RPPA.dead),
       lambdaMin = 1e-11,
       lambdaMax = 1000,
       step = 5000,
       Iaids = T,
       vertical = T,
       value = opt.RPPA.dead$optLambda)
## Perform input checks... 
## Calculating spectral condition numbers... 
## Calculating interpretational aids... 
## Plotting...

P0.RPPA.alive = sparsify(opt.RPPA.alive$optPrec, threshold = "localFDR", FDRcut=0.9)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  20 
## - Corresponding to 3.79 % of possible edges 
## 
P0.RPPA.dead = sparsify(opt.RPPA.dead$optPrec, threshold = "localFDR", FDRcut=0.9)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  53 
## - Corresponding to 10.04 % of possible edges 
## 
#dev.new(width = 20, height = 20, unit = "in", noRStudioGD = F)
set.seed(42)
Ugraph(P0.RPPA.alive$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 15, Vcex = 1, prune = T, cut = 0.5,
       main = "RPPA Array data (Surviving patients)\nFDRcutoff at 0.9, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##              [,1]        [,2]
##  [1,]  1.30312237  1.32763370
##  [2,] -0.07806325 -3.27186354
##  [3,] -1.24777005  0.39164499
##  [4,]  1.95706628 -1.51097092
##  [5,]  0.07130347 -0.38547109
##  [6,]  3.75744057 -2.68475543
##  [7,]  0.01030002 -2.26645778
##  [8,]  4.24505379 -1.94809146
##  [9,]  0.72295784 -2.97463671
## [10,]  0.88075495 -3.78946084
## [11,]  3.10337469  2.21813830
## [12,]  0.76365460 -1.35464647
## [13,] -0.03197742  0.84897079
## [14,]  3.66522379  0.67887394
## [15,]  0.42197737  1.80743202
## [16,] -0.50234629  1.80398368
## [17,]  4.42246045  0.03963785
## [18,]  2.65442045  1.33801337
Ugraph(P0.RPPA.dead$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 15, Vcex = 1, prune = T, cut = 0.5,
       main = "RPPA Array data (Deceased patients)\nFDRcutoff at 0.9, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##              [,1]       [,2]
##  [1,] -3.90505735  1.2497719
##  [2,] -1.07296319  3.5803678
##  [3,] -5.42736857  1.2229008
##  [4,] -4.03203136  0.1865667
##  [5,] -4.65516344  3.0245029
##  [6,] -2.65803881  3.1759668
##  [7,] -4.26960810  4.4250582
##  [8,] -4.36723719  1.1446965
##  [9,] -1.80386461  4.1745790
## [10,] -4.04302517  2.7518117
## [11,] -2.62154828 -0.5392755
## [12,] -0.61903568  1.7461050
## [13,] -3.80160488  2.1268026
## [14,] -5.74559053 -0.3621092
## [15,] -6.04780972  4.0601347
## [16,] -1.83739003 -0.2445880
## [17,]  0.46528983  0.5759921
## [18,]  0.04871829 -0.4501331
## [19,] -2.45560743  2.2228319
## [20,] -3.15673665  2.9376634
## [21,] -3.03439655  1.2352369
## [22,] -0.80326510 -1.2197997
## [23,] -2.23298674  5.5522509
## [24,] -1.82733616  0.9257642
## [25,] -2.03439040  2.5813589
#Ugraph(P0.RPPA.alive$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 15, Vcex = 1, prune = T, cut = 0.5,
#       main = "RPPA Array data (Surviving patients)\nFDRcutoff at 0.9, Strong Edge cutoff at 0.5")
#Ugraph(P0.RPPA.dead$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 15, Vcex = 1, prune = T, cut = 0.5,
#       main = "RPPA Array data (Deceased patients)\nFDRcutoff at 0.9, Strong Edge cutoff at 0.5")
GGM.RPPA.alive = as.data.frame(GGMnetworkStats(P0.RPPA.alive$sparseParCor, as.table = T))
## Warning in log(det(S[-j, -j] - S[-j, j, drop = FALSE] %*% S[j, -j, drop =
## FALSE]/S[j, : NaNs produced
## Warning in log(det(S[-j, -j] - S[-j, j, drop = FALSE] %*% S[j, -j, drop =
## FALSE]/S[j, : NaNs produced
## Warning in log(det(S[-j, -j] - S[-j, j, drop = FALSE] %*% S[j, -j, drop =
## FALSE]/S[j, : NaNs produced
GGM.RPPA.dead = as.data.frame(GGMnetworkStats(P0.RPPA.dead$sparseParCor, as.table = T))
GGM.RPPA.alive.order = GGM.RPPA.alive[order(GGM.RPPA.alive$degree, decreasing = T), ]
GGM.RPPA.dead.order = GGM.RPPA.dead[order(GGM.RPPA.dead$degree, decreasing = T), ]

#Output top 10
GGM.RPPA.alive.order[1:10, ]
##             degree betweenness  closeness eigenCentrality nNeg nPos mutualInfo
## GAPDH.RPPA       5        65.5 0.02631579      1.00000000    5    0        NaN
## G6PD.RPPA        3        44.0 0.02325581      0.73259940    3    0 -0.3139754
## IGFBP2.RPPA      3         6.5 0.01587302      0.14513745    3    0        NaN
## FASN.RPPA        3        36.0 0.01923077      0.17931623    3    0 -0.8602668
## PDCD4.RPPA       3         6.5 0.01587302      0.14513745    3    0        NaN
## AR.RPPA          3        54.0 0.02272727      0.23339006    3    0  1.0001768
## MYH11.RPPA       3         2.5 0.02173913      0.80492279    3    0 -2.2109076
## PREX1.RPPA       3        38.0 0.01960784      0.33451906    3    0  0.8678714
## ATM.RPPA         2        54.0 0.02500000      0.42234418    2    0  0.5043566
## TTF1.RPPA        2         0.0 0.01315789      0.09939752    2    0 -0.8732272
##                variance partialVar
## GAPDH.RPPA  -0.20806820          1
## G6PD.RPPA    0.73053696          1
## IGFBP2.RPPA -0.06925176          1
## FASN.RPPA    0.42304921          1
## PDCD4.RPPA  -0.13146993          1
## AR.RPPA      2.71876244          1
## MYH11.RPPA   0.10960113          1
## PREX1.RPPA   2.38183549          1
## ATM.RPPA     1.65591980          1
## TTF1.RPPA    0.41760169          1
GGM.RPPA.dead.order[1:10, ]
##            degree betweenness  closeness eigenCentrality nNeg nPos mutualInfo
## TFRC.RPPA      12  101.160714 0.02702703       0.8167180    8    4  0.4711292
## FASN.RPPA      11   55.960065 0.02500000       1.0000000    6    5  0.4779378
## GATA3.RPPA     10   52.022727 0.02564103       0.9511864    7    3  0.4813064
## GAPDH.RPPA      9   37.683442 0.02564103       0.8982808    5    4  0.4945405
## G6PD.RPPA       8   35.913636 0.02439024       0.8158862    5    3  0.3275210
## MYH11.RPPA      8   22.307251 0.02325581       0.8439781    4    4  0.4890627
## PDCD4.RPPA      6    5.291775 0.02173913       0.7402321    6    0  0.1991657
## MSH6.RPPA       6   13.467857 0.02325581       0.6467117    4    2  0.2125263
## ATM.RPPA        5    5.325000 0.01923077       0.4910144    4    1  0.2830009
## RBM15.RPPA      4    8.733766 0.02040816       0.3764011    2    2  0.1717378
##            variance partialVar
## TFRC.RPPA  1.601802          1
## FASN.RPPA  1.612745          1
## GATA3.RPPA 1.618187          1
## GAPDH.RPPA 1.639745          1
## G6PD.RPPA  1.387524          1
## MYH11.RPPA 1.630787          1
## PDCD4.RPPA 1.220384          1
## MSH6.RPPA  1.236799          1
## ATM.RPPA   1.327106          1
## RBM15.RPPA 1.187367          1
ggplot(GGM.RPPA.alive.order, aes(x = reorder(rownames(GGM.RPPA.alive.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.RPPA.alive.order$degree), linetype = "dashed", color = "red")  +
  geom_hline(yintercept = 1, linetype = "dashed", color = "blue")  +
  scale_x_discrete(name = "RPPA Array", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-1, Surviving Patients")

ggplot(GGM.RPPA.dead.order, aes(x = reorder(rownames(GGM.RPPA.dead.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.RPPA.dead.order$degree), linetype = "dashed", color = "red")  +
  geom_hline(yintercept = 1, linetype = "dashed", color = "blue")  +
  scale_x_discrete(name = "RPPA Array", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-1, Deceased Patients")

B. RNASeq2GeneNorm

data.R2Gn.alive = data.R2Gn[which(data.Y == 0), ]
data.R2Gn.dead = data.R2Gn[which(data.Y == 1), ]
set.seed(42)
opt.R2Gn.alive = optPenalty.kCVauto(Y = data.R2Gn.alive, lambdaMin = 1e-11, lambdaMax = 10)
opt.R2Gn.dead = optPenalty.kCVauto(Y = data.R2Gn.dead, lambdaMin = 1e-11, lambdaMax = 10)
opt.R2Gn.alive$optLambda
## [1] 1.145441
opt.R2Gn.dead$optLambda
## [1] 1.809384
edgeHeat(opt.R2Gn.alive$optPrec, diag = F, textsize = 1)
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

edgeHeat(opt.R2Gn.dead$optPrec, diag = F, textsize = 1)
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

CNplot(covML(data.R2Gn.alive),
       lambdaMin = 1e-11,
       lambdaMax = 1000,
       step = 5000,
       Iaids = T,
       vertical = T,
       value = opt.R2Gn.alive$optLambda)
## Perform input checks... 
## Calculating spectral condition numbers... 
## Calculating interpretational aids... 
## Plotting...

CNplot(covML(data.R2Gn.dead),
       lambdaMin = 1e-11,
       lambdaMax = 1000,
       step = 5000,
       Iaids = T,
       vertical = T,
       value = opt.R2Gn.dead$optLambda)
## Perform input checks... 
## Calculating spectral condition numbers... 
## Calculating interpretational aids... 
## Plotting...

Smallest possible FDRcut:

P0.R2Gn.alive.min = sparsify(opt.R2Gn.alive$optPrec, threshold = "localFDR", FDRcut=1-1e-14)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  539 
## - Corresponding to 2.76 % of possible edges 
## 
P0.R2Gn.dead.min = sparsify(opt.R2Gn.dead$optPrec, threshold = "localFDR", FDRcut=1-1e-14)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  739 
## - Corresponding to 3.79 % of possible edges 
## 
#dev.new(width = 20, height = 20, unit = "in", noRstudioGD = F)
set.seed(42)
Ugraph(P0.R2Gn.alive.min$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
       main = "RNASeq2GeneNorm data (Surviving Patients)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##              [,1]        [,2]
##   [1,]  4.8339418 -2.22741792
##   [2,]  6.2388509 -4.59779911
##   [3,]  3.9196054 -8.38430873
##   [4,]  4.7239480 -1.01065361
##   [5,]  6.7159234  0.29954974
##   [6,]  3.0659461 -0.28888263
##   [7,]  4.9284421  2.65519187
##   [8,]  1.4987280  1.33931190
##   [9,]  6.1382036 -0.58724540
##  [10,]  8.7852378  3.36598489
##  [11,]  4.2151258 -0.64568068
##  [12,]  0.3169712 -7.73128784
##  [13,]  3.2254296  5.42765454
##  [14,]  4.5916356 -0.27168793
##  [15,] 10.1223755  1.06048331
##  [16,]  5.6576941  1.47016896
##  [17,]  6.6409546 -0.71367502
##  [18,]  0.9369151 -7.87017060
##  [19,]  6.2908380  1.31326659
##  [20,]  5.5969973 -6.52510410
##  [21,] -0.7305915 -6.57858614
##  [22,]  9.6136139 -2.10069074
##  [23,]  5.8406175  0.70883725
##  [24,]  3.9324587 -7.78230315
##  [25,]  6.7972364 -0.21364725
##  [26,]  1.5876710 -1.69945417
##  [27,] -0.3051539 -7.18991693
##  [28,]  5.6069907 -2.02488228
##  [29,]  4.2482910  3.73111938
##  [30,] -1.2328020 -2.73601675
##  [31,]  5.7260344  3.82444694
##  [32,]  1.3141803 -1.25717003
##  [33,]  4.6147651 -4.79664840
##  [34,]  4.0409597 -1.52089184
##  [35,]  2.4150493  2.73958887
##  [36,] -1.5684038 -3.33775683
##  [37,]  6.2873628 -2.11270434
##  [38,]  5.0677607 -3.35947872
##  [39,]  7.3920941  1.45840527
##  [40,]  3.2955089 -1.32977375
##  [41,]  3.9588920 -6.83995502
##  [42,]  0.1660350 -5.91654271
##  [43,]  7.3676370  2.08681348
##  [44,]  4.9456797 -0.29459057
##  [45,]  5.6557729  0.25151289
##  [46,]  4.9616204  0.52819853
##  [47,]  2.7024621 -1.01232323
##  [48,]  2.7219649 -7.79312257
##  [49,]  4.3284913  5.54256061
##  [50,]  0.7150397 -1.88271137
##  [51,]  0.5894819 -1.38712389
##  [52,]  1.4175007 -0.74011265
##  [53,]  5.3926730 -1.27627708
##  [54,]  1.1253622 -2.36777366
##  [55,]  6.9413105 -3.11689562
##  [56,]  3.4359849  0.10615328
##  [57,]  2.0244515 -8.29583223
##  [58,]  2.1319504 -0.01391224
##  [59,]  1.0131440 -5.83262357
##  [60,]  5.9255516 -0.10502874
##  [61,]  6.4250837 -3.39432657
##  [62,]  4.7387457 -1.28128592
##  [63,]  5.6878739 -4.44966186
##  [64,]  2.9760557  2.68458221
##  [65,]  3.0521555 -0.84555003
##  [66,] -1.1226310 -6.03017614
##  [67,]  5.1600712 -7.77094023
##  [68,]  6.3882810  3.59785917
##  [69,]  8.1151628  3.96731470
##  [70,]  1.4138978 -8.24855505
##  [71,]  4.0907241  2.78024379
##  [72,]  4.2746106  1.14256145
##  [73,]  0.4350768 -7.11338597
##  [74,] -0.5597416 -5.68626387
##  [75,]  5.7405995 -1.50243015
##  [76,]  2.7671841 -2.21616899
##  [77,]  1.7990708  1.97104215
##  [78,]  1.6800689 -0.04499183
##  [79,] -1.4697413 -4.01933153
##  [80,]  0.1801833 -5.18737408
##  [81,]  0.9390134 -0.01471292
##  [82,]  6.3723420 -1.03504070
##  [83,] 10.6451745 -1.55756922
##  [84,] 10.9973168  0.09075175
##  [85,]  8.8924081 -0.74342994
##  [86,]  4.0504458 -0.92613051
##  [87,]  3.8775922 -2.30785448
##  [88,]  4.5493312 -1.44594996
##  [89,]  3.3441110 -7.42517938
##  [90,]  3.9504658 -2.66566896
##  [91,]  3.3247079  1.22564308
##  [92,]  5.4807472  2.40535588
##  [93,]  4.6327485 -8.17088329
##  [94,]  2.9976228 -3.77471609
##  [95,]  1.5908884 -2.77518699
##  [96,]  7.5120538  0.49911184
##  [97,]  0.6300948 -0.74280071
##  [98,]  2.5629409 -0.37168623
##  [99,]  3.7795470 -1.87170025
## [100,] 10.3009720 -0.10306836
## [101,]  6.8736093 -4.00108628
## [102,]  5.6128037 -7.40867507
## [103,] -0.2191843 -4.51475490
## [104,]  3.7393908 -0.69913014
## [105,]  5.4018807 -2.45824126
## [106,]  0.8749522 -6.56849783
## [107,]  4.4977777 -3.05073637
## [108,]  3.2372749 -8.33073178
## [109,] -0.0523965 -6.53579999
## [110,]  5.2817076 -0.25667216
## [111,]  5.9457858 -1.12730421
## [112,]  4.6509184 -0.76175206
## [113,]  4.8996358 -6.76135087
## [114,]  1.7725039 -6.48724811
## [115,]  6.1180344 -1.53675214
## [116,]  4.9836535  1.43752728
## [117,]  5.1351991 -0.69622751
## [118,]  2.6096487 -8.47729999
## [119,] -1.3942028 -5.37771766
## [120,]  9.3145923  2.66338357
## [121,]  3.8486416 -0.19509653
## [122,]  5.4306952 -1.05774613
## [123,]  5.6226442 -0.59034791
## [124,] -1.5727243 -4.64380212
## [125,]  1.2843363 -7.21794000
## [126,]  5.1056958 -1.68012481
## [127,]  4.1607160  0.29577525
## [128,]  2.7822775 -1.52869391
## [129,]  4.6659483 -1.82188424
## [130,]  4.5413667 -7.37939323
## [131,]  1.8844588 -7.58047868
## [132,]  2.8994662  3.53071241
## [133,]  6.2079425  2.32212878
## [134,]  6.1430041 -7.06257959
## [135,]  3.5346689  3.92048676
## [136,]  2.8672847  1.53445489
## [137,]  6.3659660  0.56825684
## [138,]  3.0152759 -6.61595299
## [139,] -0.8152298 -3.43947369
## [140,] -0.8534053 -4.99723758
## [141,] -0.8338246 -4.20852720
## [142,]  6.3887606 -6.39196624
## [143,]  4.1435411 -0.13345639
## [144,]  2.3743815 -7.04119878
Ugraph(P0.R2Gn.dead.min$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
       main = "RNASeq2GeneNorm data (Deceased Patients)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##               [,1]        [,2]
##   [1,]  0.60136378 -1.39732942
##   [2,]  0.59436511 -3.79246442
##   [3,] -2.17981915 -4.05947409
##   [4,] -1.18912163 -1.24802804
##   [5,]  5.20827122 -4.87732619
##   [6,]  2.29654829 -2.84069431
##   [7,] -0.34525459 -8.39858645
##   [8,] -0.34037504 -0.06225052
##   [9,] -0.01786927 -7.59005509
##  [10,]  1.77604114 -3.04941069
##  [11,]  2.43357628 -4.98519626
##  [12,] -0.11019578 -1.45012422
##  [13,]  0.80383210 -4.76120462
##  [14,]  3.35545268 -3.09141583
##  [15,] -2.57168366 -1.78231123
##  [16,]  1.57521764  0.92117748
##  [17,]  3.79529595 -5.78095713
##  [18,] -0.87200614 -5.10402273
##  [19,]  1.88525553 -1.14285168
##  [20,]  0.54059842 -2.66237538
##  [21,] -2.94351106 -3.72163059
##  [22,]  0.49744415  2.87376740
##  [23,] -0.00109703  0.32482437
##  [24,] -1.72075284 -4.88259414
##  [25,] -2.20325730  3.72412556
##  [26,] -1.94797259 -1.65073127
##  [27,]  1.23237393 -3.17113382
##  [28,]  1.27229173 -4.23034134
##  [29,]  0.38238672 -2.89367915
##  [30,]  1.93966120 -6.71278441
##  [31,] -1.55653239  3.93449162
##  [32,]  1.06085426 -0.78708964
##  [33,]  7.94378256 -3.75572145
##  [34,]  2.75726207 -1.09922561
##  [35,]  3.25332143 -1.48995420
##  [36,]  7.40109729 -5.41404314
##  [37,]  1.49665403 -5.67910592
##  [38,]  6.09550857 -1.21680766
##  [39,]  3.01540178  2.29246862
##  [40,]  2.20670557 -5.72408500
##  [41,]  1.32671272 -2.40133164
##  [42,]  0.82137485 -1.01228931
##  [43,] -3.63954496 -6.27077966
##  [44,]  0.62251535 -2.08955601
##  [45,] -1.36512888 -3.00985635
##  [46,]  3.91176841 -1.72507052
##  [47,]  1.02906504 -2.14035075
##  [48,]  0.79077806 -2.83351372
##  [49,]  5.08185118 -1.50558197
##  [50,]  1.21599210 -1.97092052
##  [51,]  1.21067317 -1.31275770
##  [52,] -3.16505805  2.96420653
##  [53,] -0.93176604 -2.81332054
##  [54,]  5.07874975 -3.63488840
##  [55,]  4.34743297 -3.03717500
##  [56,] -2.74693026  3.36483156
##  [57,]  3.48456511 -4.17397757
##  [58,] -2.48826432 -7.39626921
##  [59,]  5.46903680 -2.51512447
##  [60,] -0.14326601 -0.36621231
##  [61,] -3.40146656 -1.44224045
##  [62,]  2.73516310 -4.43813216
##  [63,]  2.18552300 -1.14105094
##  [64,]  0.48027250  1.95066826
##  [65,] -0.58538378 -5.88853172
##  [66,]  0.79435809 -1.76460979
##  [67,] -0.43749083 -4.03663348
##  [68,]  1.33052141 -0.95397311
##  [69,]  0.41087379 -2.19081062
##  [70,]  6.53746433 -6.48309715
##  [71,]  0.00537019 -2.69685456
##  [72,]  2.84099114 -2.65872165
##  [73,] -4.04567881 -5.59770118
##  [74,]  3.58398043 -7.25411733
##  [75,]  0.68154509  0.08608182
##  [76,]  3.96178924 -3.52357954
##  [77,]  4.05764991 -2.55956199
##  [78,] -3.95994626  2.07537647
##  [79,]  5.68427255 -0.52901332
##  [80,]  4.74583590 -6.45901964
##  [81,]  1.37600481 -0.25847053
##  [82,]  1.58481122 -1.43378046
##  [83,]  7.95829789 -2.42932177
##  [84,]  3.71679835  2.02799325
##  [85,]  2.81289319 -3.25152411
##  [86,]  3.55269933 -6.54476065
##  [87,] -1.10332491 -1.70275768
##  [88,] -0.83214436 -2.15945319
##  [89,] -1.59862230 -5.88662841
##  [90,]  7.01198700 -5.92007529
##  [91,] -2.38179354 -5.15401300
##  [92,] -4.26902846  1.44520698
##  [93,]  2.28222078  0.49031051
##  [94,]  2.37892782 -1.43791720
##  [95,]  7.99624474 -3.13398194
##  [96,]  3.04250974 -2.13933169
##  [97,]  5.17376950 -3.05366147
##  [98,] -1.13959284 -0.83299843
##  [99,]  1.56932374 -2.61377073
## [100,]  7.88458865 -4.35693914
## [101,] -0.19489660 -2.03301463
## [102,]  1.80444767 -0.00688292
## [103,]  3.82110758  3.60030917
## [104,]  2.34291984 -3.75565967
## [105,] -1.09659619 -0.48032157
## [106,]  1.79128545 -0.71988710
## [107,]  0.15551843 -3.82991950
## [108,]  0.79153896 -6.22549127
## [109,]  4.64593324  0.34091124
## [110,] -0.13471479 -0.90306451
## [111,]  0.93864886 -3.76319812
## [112,] -1.98968829 -3.58933538
## [113,]  1.35531008  1.56399492
## [114,]  2.53322389 -1.72584053
## [115,]  7.55660848 -4.83294507
## [116,] -4.36889478 -4.81985908
## [117,]  2.52250504 -6.66218719
## [118,]  0.09118475 -1.86734524
## [119,]  4.33147204 -5.15350762
## [120,] -1.31264467 -7.18798419
## [121,]  0.34472370 -5.50033190
## [122,]  4.01259418  1.19062746
## [123,]  1.66867146 -3.91414521
## [124,]  2.42317404 -2.39958063
## [125,] -0.62476291 -1.18059775
## [126,] -3.12334936 -6.85238758
## [127,]  2.04106328 -2.25942868
## [128,]  2.84542622 -5.53801053
## [129,]  2.96415656 -0.21173630
## [130,]  2.96350814 -0.60487112
## [131,]  1.52716740 -0.75335927
## [132,]  2.21811143 -3.20852327
## [133,]  0.88263433  1.08961936
## [134,]  2.95505586  3.81048967
## [135,]  5.68104271  2.06148966
## [136,] -3.57490105  2.54974284
## [137,]  1.59685835 -1.78715858
## [138,]  1.76621723 -2.45008216
## [139,]  1.53355953 -3.49976242
## [140,]  2.03413499 -1.83750703
## [141,]  0.19436244 -3.45218829
## [142,] -1.61975410 -0.11179186
## [143,] -0.61727219 -2.59767792
## [144,]  5.44065796 -0.05177114
## [145,]  5.64835419 -4.14119551
## [146,]  1.67803916 -4.75448472
## [147,] -0.20154914  2.64049214
## [148,] -0.50659860 -2.04987702
## [149,]  2.35290835 -0.62916397
## [150,]  5.01005077  2.70515557
## [151,] -0.93737252  1.31451215
## [152,] -0.65296651 -3.16242448
## [153,]  4.24529382 -1.14633663
## [154,]  2.14934313  4.01100831
## [155,] -1.63250277 -2.51694161
## [156,]  3.13386081 -2.58439217
## [157,]  0.94880860 -2.51218261
#Ugraph(P0.R2Gn.alive.min$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
#       main = "RNASeq2GeneNorm data (Surviving Patiens)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5")
#Ugraph(P0.R2Gn.dead.min$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
#       main = "RNASeq2GeneNorm data (Deceased Patiens)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5")
GGM.R2Gn.alive.min = as.data.frame(GGMnetworkStats(P0.R2Gn.alive.min$sparseParCor, as.table = T))
GGM.R2Gn.dead.min = as.data.frame(GGMnetworkStats(P0.R2Gn.dead.min$sparseParCor, as.table = T))
GGM.R2Gn.alive.min.order = GGM.R2Gn.alive.min[order(GGM.R2Gn.alive.min$degree, decreasing = T), ]
GGM.R2Gn.dead.min.order = GGM.R2Gn.dead.min[order(GGM.R2Gn.dead.min$degree, decreasing = T), ]

#Output top 5%
GGM.R2Gn.alive.min.order[1:round(nrow(GGM.R2Gn.alive.min.order) * 0.05), ]
##             degree betweenness   closeness eigenCentrality nNeg nPos mutualInfo
## GAPDH.R2Gn     104   6320.1007 0.005464481       1.0000000   47   57 0.21637603
## SQSTM1.R2Gn     61   1284.8660 0.004424779       0.8860031   35   26 0.09878604
## EEF2.R2Gn       41    871.3416 0.004032258       0.6698942   25   16 0.05263190
## TGM2.R2Gn       38    909.1790 0.004016064       0.6032739   17   21 0.04378580
## HSPA1A.R2Gn     38    487.9173 0.004032258       0.6811141   19   19 0.04737123
## IGFBP2.R2Gn     34    565.8591 0.003891051       0.6422618   21   13 0.04466094
## COL6A1.R2Gn     31    198.4089 0.003921569       0.6821071   15   16 0.03482934
## SYP.R2Gn        27    265.7875 0.003816794       0.5227951   15   12 0.02869397
## CTNNB1.R2Gn     25    108.3801 0.003731343       0.5874538   13   12 0.02540837
## CDH2.R2Gn       24    183.0774 0.003105590       0.4971453   16    8 0.01769934
##             variance partialVar
## GAPDH.R2Gn  1.241569          1
## SQSTM1.R2Gn 1.103830          1
## EEF2.R2Gn   1.054042          1
## TGM2.R2Gn   1.044759          1
## HSPA1A.R2Gn 1.048511          1
## IGFBP2.R2Gn 1.045673          1
## COL6A1.R2Gn 1.035443          1
## SYP.R2Gn    1.029110          1
## CTNNB1.R2Gn 1.025734          1
## CDH2.R2Gn   1.017857          1
GGM.R2Gn.dead.min.order[1:round(nrow(GGM.R2Gn.dead.min.order) * 0.05), ]
##             degree betweenness   closeness eigenCentrality nNeg nPos mutualInfo
## FN1.R2Gn        81   2798.3855 0.004201681       1.0000000   49   32  0.4513300
## SYP.R2Gn        68   2016.8866 0.004081633       0.8879855   38   30  0.2169417
## EEF2.R2Gn       66   1399.8347 0.004016064       0.9268474   37   29  0.1837564
## GAPDH.R2Gn      62   1487.3920 0.003921569       0.8463917   33   29  0.2305904
## HSPA1A.R2Gn     61   1355.8966 0.003984064       0.8528902   37   24  0.2671855
## CTNNB1.R2Gn     58   1741.2118 0.003816794       0.7374888   29   29  0.1455934
## SQSTM1.R2Gn     57    960.8674 0.003906250       0.8354205   28   29  0.2015885
## RPS6.R2Gn       52   1082.3675 0.003802281       0.7763729   29   23  0.1746873
## CCND1.R2Gn      40    284.9175 0.003663004       0.7346627   19   21  0.1312334
## ADAR.R2Gn       37    273.7968 0.003496503       0.6672372   20   17  0.0903635
##             variance partialVar
## FN1.R2Gn    1.570399          1
## SYP.R2Gn    1.242272          1
## EEF2.R2Gn   1.201723          1
## GAPDH.R2Gn  1.259343          1
## HSPA1A.R2Gn 1.306283          1
## CTNNB1.R2Gn 1.156726          1
## SQSTM1.R2Gn 1.223345          1
## RPS6.R2Gn   1.190874          1
## CCND1.R2Gn  1.140234          1
## ADAR.R2Gn   1.094572          1
ggplot(GGM.R2Gn.alive.min.order, aes(x = reorder(rownames(GGM.R2Gn.alive.min.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.R2Gn.alive.min.order$degree), linetype = "dashed", color = "red")  +
  # 10th unit: top 5%
  geom_hline(yintercept = GGM.R2Gn.alive.min.order[10,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-14, Surviving Patients")

ggplot(GGM.R2Gn.dead.min.order, aes(x = reorder(rownames(GGM.R2Gn.dead.min.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.R2Gn.dead.min.order$degree), linetype = "dashed", color = "red")  +
  # 10th unit: top 5%
  geom_hline(yintercept = GGM.R2Gn.dead.min.order[10,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-14, Deceased Patients")

FDRcut 1-1e-6:

P0.R2Gn.alive.6 = sparsify(opt.R2Gn.alive$optPrec, threshold = "localFDR", FDRcut= 1-1e-6)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  883 
## - Corresponding to 4.53 % of possible edges 
## 
P0.R2Gn.dead.6 = sparsify(opt.R2Gn.dead$optPrec, threshold = "localFDR", FDRcut= 1-1e-6)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  1213 
## - Corresponding to 6.22 % of possible edges 
## 
#dev.new(width = 20, height = 20, unit = "in", noRstudioGD = F)
set.seed(42)
Ugraph(P0.R2Gn.alive.6$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
       main = "RNASeq2GeneNorm data (Surviving Patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##              [,1]        [,2]
##   [1,]  0.6626125  0.22468911
##   [2,] -2.9474226 -2.44441443
##   [3,]  1.4140883 -2.87541140
##   [4,] -4.8168754  1.00793539
##   [5,] -0.3444577  0.20897916
##   [6,] -2.6715026 -0.05995879
##   [7,] -1.1045375 -1.04594934
##   [8,] -3.4172919  1.11422809
##   [9,] -4.2517857  3.01169176
##  [10,]  0.4390078  0.09729709
##  [11,] -4.5552175 -0.91324303
##  [12,] -1.1616016  0.22413829
##  [13,]  2.0052218 -1.65962093
##  [14,] -4.9010147 -2.32009518
##  [15,] -2.5078665 -0.91657816
##  [16,]  3.3874139  4.05328681
##  [17,] -2.9084504 -0.68100239
##  [18,] -1.7135563 -0.20514625
##  [19,]  5.1966533 -3.54389476
##  [20,]  0.1809289 -0.73117953
##  [21,]  0.2133673 -2.12637691
##  [22,]  4.9613471 -4.39907119
##  [23,] -3.0074818  3.37628123
##  [24,] -6.7230637 -3.80625426
##  [25,] -2.3675244  1.06511317
##  [26,] -1.4226696 -3.93925624
##  [27,] -1.4497001 -0.90844565
##  [28,]  1.1085179 -1.44820872
##  [29,]  1.1289153 -6.55621704
##  [30,] -1.0743340  1.34388257
##  [31,] -4.6130038  0.40219481
##  [32,]  0.7779442 -4.55233484
##  [33,] -0.0832558 -3.72920913
##  [34,] -4.6459028  0.01947182
##  [35,]  1.7312776 -0.26939100
##  [36,]  3.0475157  0.55761072
##  [37,]  0.1122174 -2.68148227
##  [38,] -1.2791569  1.00420260
##  [39,] -3.2982086  2.12327265
##  [40,]  1.7816080 -1.04241869
##  [41,] -0.3846561 -0.33008214
##  [42,] -2.3897499 -1.82406918
##  [43,] -2.7422829  1.92866744
##  [44,] -0.7908125 -1.71373708
##  [45,]  3.2368339 -5.27277029
##  [46,] -3.2674246 -1.62733235
##  [47,]  1.3355722  3.64663855
##  [48,] -2.0628172  0.01543025
##  [49,] -2.3458896  0.28874152
##  [50,] -1.8833103  0.61539564
##  [51,] -0.6708106  1.14361842
##  [52,] -2.4084407 -5.03064827
##  [53,] -3.4069402 -3.33535254
##  [54,] -2.1694505  5.51845721
##  [55,] -1.6185683 -5.26283331
##  [56,] -2.5547981 -2.24674843
##  [57,] -7.1392644 -2.89706777
##  [58,]  1.0296150 -0.14830118
##  [59,]  0.1034231 -6.78190768
##  [60,] -0.2492421  1.39002543
##  [61,] -1.9598868 -0.64665638
##  [62,]  0.7105744 -0.72059639
##  [63,]  0.8725143  1.60729706
##  [64,] -1.8036370  0.91000580
##  [65,]  2.0231064 -2.24471793
##  [66,]  0.5391788  1.89373383
##  [67,]  5.6441216 -1.26746925
##  [68,] -1.8060333 -1.09022859
##  [69,] -1.0387251  1.72568452
##  [70,] -0.2266317 -0.01100187
##  [71,] -2.2316883 -2.74211454
##  [72,] -0.2080312 -1.37140807
##  [73,]  2.9844693 -6.53755395
##  [74,] -0.1739923 -0.56516390
##  [75,]  3.5002154 -6.12994762
##  [76,]  4.9394258 -5.08629899
##  [77,]  2.3734597 -6.73439061
##  [78,] -6.0922922 -1.04246092
##  [79,] -1.9609603 -4.97608346
##  [80,] -1.0867523  4.21219255
##  [81,]  3.8304934 -1.29191614
##  [82,] -6.8030959  2.37538406
##  [83,]  1.6558204  1.67302222
##  [84,] -3.9050025  1.00251785
##  [85,] -2.5332779 -1.41459149
##  [86,] -4.8094717 -4.49078935
##  [87,]  2.3316812 -0.81134321
##  [88,]  3.8660393 -5.48451755
##  [89,] -1.8471057 -1.85417945
##  [90,] -1.0761207 -1.62955286
##  [91,] -1.3717205  2.44812531
##  [92,] -0.6173523 -2.52973554
##  [93,]  4.2323817 -5.88744363
##  [94,] -0.3533584 -4.58413212
##  [95,]  2.8795135  1.68105600
##  [96,] -2.0103998 -4.06777873
##  [97,] -1.7974099  0.32730188
##  [98,]  5.6958785 -3.16628270
##  [99,] -2.0838620  2.58234784
## [100,] -4.2974548 -1.51990380
## [101,] -0.5729691  2.60962777
## [102,] -0.8142015 -0.53367893
## [103,] -3.0192971 -0.29495962
## [104,] -1.6960477 -0.45439524
## [105,]  0.7070984 -6.91669210
## [106,]  0.7660227  0.66562673
## [107,] -1.2435573  5.64858369
## [108,] -3.0381825  0.37059329
## [109,] -2.2880366 -0.59179953
## [110,]  2.2812470 -6.02231227
## [111,]  0.7811006 -1.98174939
## [112,] -1.8237541 -3.01699525
## [113,] -1.6746779 -2.40962758
## [114,]  0.6108266 -3.07248131
## [115,] -1.0761995 -2.59353440
## [116,]  3.7915041 -4.69687653
## [117,] -0.0723502  0.95712931
## [118,]  1.4886033  0.24344837
## [119,] -6.0013715 -0.44082896
## [120,]  0.6798923  3.90446780
## [121,] -0.7848846 -3.88465612
## [122,]  3.6919156 -1.81169087
## [123,]  5.5134963 -1.87855555
## [124,]  4.3839081 -5.02680621
## [125,] -0.6467793 -0.85944098
## [126,]  0.3177152  0.94777153
## [127,]  3.9361621 -0.09336302
## [128,] -0.5472480  1.64780797
## [129,]  2.6891176  1.36981736
## [130,]  1.6286578  0.86475858
## [131,] -1.0277246 -3.10151781
## [132,] -1.4304181 -0.19620758
## [133,] -1.1057923 -1.34163268
## [134,] -1.1238583  0.47056531
## [135,]  0.1136398  2.02723621
## [136,]  3.8531213 -0.79953343
## [137,] -1.8443485  1.30266529
## [138,] -2.0849315 -1.33687773
## [139,] -0.9549265 -0.78643667
## [140,] -1.1556349 -5.09782782
## [141,]  5.5010625 -4.03027059
## [142,] -4.9509551  3.01931603
## [143,]  0.2962771  5.34700416
## [144,]  4.6274547 -3.51865941
## [145,] -0.8026434  0.01369267
## [146,] -1.4413156 -0.62988288
## [147,] -2.2362135 -0.89780158
## [148,] -2.8132300 -4.70951189
## [149,]  4.3499917 -4.19968078
## [150,] -0.3962128 -0.90097141
## [151,] -0.7334097  0.43607723
## [152,] -1.5021528  0.08608431
## [153,] -0.4459254  0.90075408
## [154,]  1.7153064 -6.32025526
## [155,] -3.0511929  5.25622245
## [156,]  5.1996984 -2.69607381
## [157,] -4.6137941  1.35077931
## [158,] -3.8718296  1.92239498
## [159,]  2.8294064 -5.79993800
## [160,] -2.5277754  0.50498178
## [161,] -2.1267187  2.00023099
## [162,]  2.3331812  2.02644846
## [163,]  4.1674580  3.34689725
## [164,] -1.4399198 -1.26237032
## [165,] -2.6101930 -3.73401098
## [166,]  4.2173051 -0.48084627
## [167,] -4.1282302 -0.22828612
## [168,]  3.9934899  0.42804043
## [169,]  2.7972917  0.96155663
## [170,]  1.6741885 -6.96817023
## [171,]  2.2852084  0.05362223
## [172,] -1.0928814  0.69821003
## [173,]  5.7992164 -2.40254935
Ugraph(P0.R2Gn.dead.6$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
       main = "RNASeq2GeneNorm data (Deceased Patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##               [,1]        [,2]
##   [1,] -1.08124046 -2.06484802
##   [2,] -1.66915249 -3.73180174
##   [3,] -0.29119982  0.69835515
##   [4,] -2.17088815 -2.76791541
##   [5,] -1.67520177 -5.86662252
##   [6,] -1.47303743 -2.08236067
##   [7,]  3.57431264 -0.24941017
##   [8,]  1.03585723 -2.89733786
##   [9,]  4.37268212 -5.55427976
##  [10,]  0.67179072 -3.15876915
##  [11,] -2.93225520 -2.02486845
##  [12,]  0.43071694 -2.70856766
##  [13,] -0.53560550 -0.54631388
##  [14,] -2.54995686 -2.84195545
##  [15,] -1.90458360  0.25534346
##  [16,] -0.74671986 -5.23251328
##  [17,] -2.10026850 -3.69441102
##  [18,] -2.13716156 -2.09810913
##  [19,]  1.30221313 -0.29957954
##  [20,] -0.26561320 -4.07817533
##  [21,]  0.02438068 -2.20104271
##  [22,] -4.18224699 -0.37668433
##  [23,]  4.82947483 -5.09912520
##  [24,]  0.88504600  0.41640588
##  [25,]  2.13513573 -3.86333295
##  [26,] -0.82180465 -4.53876190
##  [27,]  2.63888230  0.67212675
##  [28,]  2.42806923 -2.65657213
##  [29,] -7.38872944 -4.54309047
##  [30,] -2.63533972 -3.94695730
##  [31,] -2.60807735 -8.71927985
##  [32,] -0.14461879 -2.60723330
##  [33,]  1.29907771 -2.61301018
##  [34,] -0.56357907 -1.29677120
##  [35,] -4.60599718 -3.67958893
##  [36,] -3.91795405 -0.68939156
##  [37,] -0.67531369 -3.26755524
##  [38,] -6.98869505 -4.88285926
##  [39,] -4.90196891 -3.32148790
##  [40,] -0.48986833 -3.94596935
##  [41,] -1.71649649 -0.19073019
##  [42,]  5.17667055 -3.25053448
##  [43,] -2.61743398 -5.54990365
##  [44,] -2.58000130  0.36168094
##  [45,] -0.95326473 -5.78604447
##  [46,] -4.10105642 -1.57263927
##  [47,] -2.03437695 -4.12003537
##  [48,] -1.15151047 -2.89995616
##  [49,] -7.58862056 -2.72433672
##  [50,] -0.39845138 -3.58821347
##  [51,] -4.31885057 -2.16796444
##  [52,] -0.75217684 -1.35880032
##  [53,] -0.95416327  3.46914681
##  [54,]  0.86264301 -0.58180117
##  [55,] -2.32590497 -0.32213660
##  [56,] -0.68324924 -2.59308800
##  [57,] -0.10187853 -2.84518512
##  [58,] -3.48030000 -3.13870822
##  [59,] -0.88812663 -2.82534038
##  [60,] -0.15356777 -3.80762597
##  [61,] -1.19371232  2.28823141
##  [62,]  0.32748783 -8.29086400
##  [63,]  0.30460150 -1.39708869
##  [64,] -3.15081643 -5.86427990
##  [65,] -2.59298556 -2.32386171
##  [66,] -1.99821647 -6.43405049
##  [67,] -0.86868070  0.35430529
##  [68,] -3.10607030  3.02936631
##  [69,] -2.46362657  0.01327254
##  [70,] -2.07591762  0.82677645
##  [71,] -0.62337812 -3.04296689
##  [72,]  0.87259432  0.98296732
##  [73,]  0.79549603 -2.39143097
##  [74,] -1.29089482 -3.72657698
##  [75,] -0.19222987  1.07143877
##  [76,]  2.24315514 -0.85671689
##  [77,] -0.57433942 -2.30314605
##  [78,]  0.42818813 -0.70101456
##  [79,] -0.97315149 -3.98441435
##  [80,] -0.60720995 -1.54776607
##  [81,] -5.33677744 -0.57716298
##  [82,]  0.13433981 -1.59280588
##  [83,]  2.68464646  0.39889108
##  [84,] -1.36845841 -2.72384008
##  [85,] -4.40064509  2.45703330
##  [86,]  3.34443529 -4.15562494
##  [87,]  1.40248234 -3.18662119
##  [88,] -1.16546490 -4.38779920
##  [89,]  3.52685580 -7.01453072
##  [90,] -1.98381042 -1.38742204
##  [91,]  1.68280445 -1.31558098
##  [92,] -3.01419467 -0.46645104
##  [93,] -3.30522575 -3.84776066
##  [94,]  0.15272410 -4.04054652
##  [95,]  0.26544334 -3.32269748
##  [96,] -7.35570394 -1.36701966
##  [97,] -7.40307513 -3.28718205
##  [98,] -0.72335519 -6.33700134
##  [99,] -2.96326472 -3.17161549
## [100,] -3.08134470 -1.26507827
## [101,] -1.73803598 -4.45697986
## [102,]  0.66679080 -1.34136292
## [103,]  1.76944571 -0.41453382
## [104,] -6.74192527 -5.68607412
## [105,]  0.76983021  0.03368888
## [106,] -3.86023840  0.51501041
## [107,]  1.13064701 -4.58725690
## [108,] -1.19911000 -3.36074482
## [109,] -4.85377225 -2.65638850
## [110,] -2.13952506 -0.77594985
## [111,] -4.20446489 -4.90097917
## [112,] -0.11365707 -0.39463804
## [113,]  1.69875901 -7.78408759
## [114,] -0.95960672 -2.39980624
## [115,] -3.09832760 -1.68851408
## [116,] -1.04137261 -1.10670896
## [117,] -7.52744804 -3.89666896
## [118,]  0.14628663 -4.90893134
## [119,] -0.97396052  1.37837624
## [120,] -1.51952642 -1.23164048
## [121,]  1.95750827 -3.48036946
## [122,] -7.58200194 -2.07428749
## [123,] -1.32128911 -1.83795718
## [124,]  0.07714465 -0.84136911
## [125,]  1.25511530 -5.23596042
## [126,]  1.97997552 -5.19766630
## [127,] -0.44274444 -0.91258938
## [128,] -0.09057958 -1.90811004
## [129,]  2.76570051 -1.96358931
## [130,] -0.48121193 -5.56084107
## [131,] -2.31294157 -3.26227176
## [132,] -3.59275434 -6.12837396
## [133,] -1.55608871  2.08399535
## [134,] -3.12120982 -4.53756031
## [135,]  0.56297147 -3.42093622
## [136,] -1.15471187  0.19085991
## [137,]  3.86677259 -3.89745986
## [138,]  1.77999440 -4.56020781
## [139,]  1.53939661 -4.06398618
## [140,] -2.30489124  3.36234026
## [141,] -0.83786770 -0.36235807
## [142,] -1.60541605 -3.37775370
## [143,]  1.60156371 -1.81389853
## [144,] -3.69620999  2.99295299
## [145,] -0.33926984 -2.82853997
## [146,] -1.28794997 -0.22730747
## [147,] -0.13735924 -5.83293311
## [148,]  0.51526664 -3.79121994
## [149,]  0.01604318 -3.53551724
## [150,] -0.92980082 -3.71802486
## [151,]  2.66751632 -1.15067499
## [152,] -1.64501731 -5.19065895
## [153,]  0.48079849  0.53668874
## [154,] -4.69288891 -5.96844986
## [155,]  2.20697787 -6.06025787
## [156,]  0.20131191 -3.01573539
## [157,] -0.14392554 -3.31335582
## [158,]  0.16485717 -1.91293611
## [159,]  3.79224399 -1.99463365
## [160,] -2.38026660 -1.64987387
## [161,] -1.12824041 -1.59631572
## [162,] -2.70241969 -4.83125693
## [163,] -1.81617547 -2.86777711
## [164,] -3.66967577 -1.94627568
## [165,] -3.25042051 -0.99310058
## [166,] -2.65378171 -6.27045207
## [167,]  1.04169326 -1.69094286
## [168,] -3.31500061  0.22599407
## [169,]  0.84710285 -2.01475098
## [170,] -4.54457537 -4.29304495
## [171,] -0.47384444 -4.55737992
## [172,]  0.06929054 -7.34786166
## [173,]  0.74504961 -5.87891021
## [174,]  1.44390101 -2.16642216
## [175,] -3.56308691 -4.11099825
## [176,] -3.88737872 -2.96691225
## [177,]  3.29473862 -2.67104168
## [178,]  1.04686514 -1.05509293
## [179,] -1.77014304 -2.34128513
## [180,] -0.30909952 -1.86700354
#Ugraph(P0.R2Gn.alive.6$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
#       main = "RNASeq2GeneNorm data (Surviving Patiens)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")
#Ugraph(P0.R2Gn.dead.6$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
#       main = "RNASeq2GeneNorm data (Deceased Patiens)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")
GGM.R2Gn.alive.6 = as.data.frame(GGMnetworkStats(P0.R2Gn.alive.6$sparseParCor, as.table = T))
GGM.R2Gn.dead.6 = as.data.frame(GGMnetworkStats(P0.R2Gn.dead.6$sparseParCor, as.table = T))
GGM.R2Gn.alive.6.order = GGM.R2Gn.alive.6[order(GGM.R2Gn.alive.6$degree, decreasing = T), ]
GGM.R2Gn.dead.6.order = GGM.R2Gn.dead.6[order(GGM.R2Gn.dead.6$degree, decreasing = T), ]

#Output top 5%
GGM.R2Gn.alive.6.order[1:round(nrow(GGM.R2Gn.alive.6.order) * 0.05), ]
##             degree betweenness   closeness eigenCentrality nNeg nPos mutualInfo
## GAPDH.R2Gn     129   7198.8489 0.004651163       1.0000000   58   71 0.21508889
## SQSTM1.R2Gn     81   2049.3061 0.003787879       0.8321434   43   38 0.10067626
## IGFBP2.R2Gn     62   1283.3031 0.003521127       0.6934990   35   27 0.05000158
## TGM2.R2Gn       62   1482.6744 0.003546099       0.6306330   34   28 0.04764892
## HSPA1A.R2Gn     53    512.0792 0.003401361       0.6909312   24   29 0.04861295
## EEF2.R2Gn       50    723.6905 0.003378378       0.6365496   27   23 0.05356719
## COL6A1.R2Gn     48    290.2947 0.003367003       0.6954044   24   24 0.03679409
## ERBB3.R2Gn      44    573.6492 0.003300330       0.4926954   17   27 0.02392273
## SYP.R2Gn        43    491.2374 0.003289474       0.5758292   23   20 0.03093353
## CDH2.R2Gn       40    196.6319 0.003205128       0.5904205   21   19 0.01957613
##             variance partialVar
## GAPDH.R2Gn  1.239972          1
## SQSTM1.R2Gn 1.105919          1
## IGFBP2.R2Gn 1.051273          1
## TGM2.R2Gn   1.048802          1
## HSPA1A.R2Gn 1.049814          1
## EEF2.R2Gn   1.055028          1
## COL6A1.R2Gn 1.037479          1
## ERBB3.R2Gn  1.024211          1
## SYP.R2Gn    1.031417          1
## CDH2.R2Gn   1.019769          1
GGM.R2Gn.dead.6.order[1:round(nrow(GGM.R2Gn.dead.6.order) * 0.05), ]
##             degree betweenness   closeness eigenCentrality nNeg nPos mutualInfo
## FN1.R2Gn       103   3102.4423 0.003891051       1.0000000   61   42  0.4503771
## GAPDH.R2Gn      94   2170.2085 0.003745318       0.9484897   49   45  0.2350435
## SYP.R2Gn        91   1903.2029 0.003745318       0.9517211   51   40  0.2169166
## SQSTM1.R2Gn     90   1505.2766 0.003731343       0.9930780   45   45  0.1992638
## HSPA1A.R2Gn     86   1774.1413 0.003676471       0.9177083   53   33  0.2687390
## EEF2.R2Gn       85   1506.3636 0.003623188       0.9587648   47   38  0.1831855
## CTNNB1.R2Gn     76   1340.7157 0.003448276       0.8074700   43   33  0.1447713
## RPS6.R2Gn       75   1165.9741 0.003496503       0.8430660   42   33  0.1783714
## ADAR.R2Gn       58    366.7931 0.003289474       0.7450061   32   26  0.0918566
## CCND1.R2Gn      52    308.7403 0.003267974       0.7207221   23   29  0.1315518
##             variance partialVar
## FN1.R2Gn    1.568904          1
## GAPDH.R2Gn  1.264964          1
## SYP.R2Gn    1.242240          1
## SQSTM1.R2Gn 1.220504          1
## HSPA1A.R2Gn 1.308314          1
## EEF2.R2Gn   1.201037          1
## CTNNB1.R2Gn 1.155775          1
## RPS6.R2Gn   1.195269          1
## ADAR.R2Gn   1.096208          1
## CCND1.R2Gn  1.140597          1
ggplot(GGM.R2Gn.alive.6.order, aes(x = reorder(rownames(GGM.R2Gn.alive.6.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.R2Gn.alive.6.order$degree), linetype = "dashed", color = "red")  +
  # 10th unit: top 5%
  geom_hline(yintercept = GGM.R2Gn.alive.6.order[10,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-6, Surviving Patients")

ggplot(GGM.R2Gn.dead.6.order, aes(x = reorder(rownames(GGM.R2Gn.dead.6.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.R2Gn.dead.6.order$degree), linetype = "dashed", color = "red")  +
  # 10th unit: top 5%
  geom_hline(yintercept = GGM.R2Gn.dead.6.order[10,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-6, Deceased Patients")

C. miRNASeqGene

data.mRSG.alive = data.mRSG[which(data.Y == 0), ]
data.mRSG.dead = data.mRSG[which(data.Y == 1), ]
set.seed(42)
opt.mRSG.alive = optPenalty.kCVauto(Y = data.mRSG.alive, lambdaMin = 1e-11, lambdaMax = 10)
opt.mRSG.alive$optLambda
## [1] 1.662094
set.seed(42)

# Warning: If lambdaMax is set to 1000, lambda will be stuck at 1000
# Setting lambdaMax = 10 avoids this issue

opt.mRSG.dead = optPenalty.kCVauto(Y = data.mRSG.dead, lambdaMin = 1e-11, lambdaMax = 10)
#opt.mRSG.dead.10 = optPenalty.kCVauto(Y = data.mRSG.dead, lambdaMin = 1e-11, lambdaMax = 1000, fold = 10)
#opt.mRSG.dead.5 = optPenalty.kCVauto(Y = data.mRSG.dead, lambdaMin = 1e-11, lambdaMax = 1000, fold = 5)
#opt.mRSG.dead.3 = optPenalty.kCVauto(Y = data.mRSG.dead, lambdaMin = 1e-11, lambdaMax = 1000, fold = 3)
#setNames(c(3,5,10,43), c(opt.mRSG.dead.3$optLambda, opt.mRSG.dead.5$optLambda, opt.mRSG.dead.10$optLambda, opt.mRSG.dead$optLambda))
opt.mRSG.dead$optLambda
## [1] 1.079529
edgeHeat(opt.mRSG.alive$optPrec, diag = F, textsize = 1)
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

edgeHeat(opt.mRSG.dead$optPrec, diag = F, textsize = 1)
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

Smallest possible FDRcut:

P0.mRSG.alive.min = sparsify(opt.mRSG.alive$optPrec, threshold = "localFDR", FDRcut=1-1e-13)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  1517 
## - Corresponding to 1.38 % of possible edges 
## 
P0.mRSG.dead.min = sparsify(opt.mRSG.dead$optPrec, threshold = "localFDR", FDRcut=1-1e-13)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  1684 
## - Corresponding to 1.53 % of possible edges 
## 
set.seed(42)
Ugraph(P0.mRSG.alive.min$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 2, Vcex = .3, prune = T, cut = 0.5, main = "miRNASeqGene data (Surviving Patients)\nFDRcutoff at 1-1e-13, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##                [,1]        [,2]
##   [1,]  4.935464995 -0.95010258
##   [2,] -6.356090756  7.87346558
##   [3,] -2.575691947  1.38449284
##   [4,] -3.794261835 -0.43068326
##   [5,] -0.446362060  3.30032005
##   [6,] -5.460130722 -3.59724432
##   [7,] -4.966215468 -3.29126334
##   [8,] -1.579816239 -2.55498813
##   [9,] -3.001379615 -2.53600590
##  [10,] -1.996251039  2.55458836
##  [11,]  1.899743005 -0.86603179
##  [12,]  0.585340317  0.63579584
##  [13,] -2.336666951  0.82141166
##  [14,] -0.596473373  1.27240717
##  [15,] -0.030091979  2.91167239
##  [16,] -1.669723998  0.77049357
##  [17,] -1.898613089  3.33064813
##  [18,]  1.526658613 -1.86507839
##  [19,] -7.070045706 -0.61683749
##  [20,] -1.772002372  1.83993354
##  [21,] -8.341486045 -2.39103124
##  [22,] -3.108964900  1.82752410
##  [23,]  1.762183582  3.59002519
##  [24,] -2.258129786  4.41582689
##  [25,] -3.044241288  4.03801119
##  [26,] -1.789130043  2.69232949
##  [27,] -3.596002290  0.80065708
##  [28,] -3.360005205  5.29258786
##  [29,] -4.155062202  2.07095976
##  [30,]  1.536848928  1.56354971
##  [31,] -0.863211998  1.70901113
##  [32,] -3.085639725  3.28991342
##  [33,] -0.788624643  4.36180405
##  [34,] -3.843876240  1.60917792
##  [35,] -1.201562336  1.91085409
##  [36,] -1.168230191  0.24278027
##  [37,] -0.941050994 -0.94353871
##  [38,] -0.532674575  1.95165122
##  [39,]  0.779851022  1.78829495
##  [40,] -2.928890111  2.23445458
##  [41,] -0.952512418  3.24869286
##  [42,]  0.726060581  3.34790648
##  [43,] -7.643683295  3.09336019
##  [44,]  1.002003046  7.44349735
##  [45,] -2.384535324  1.21264626
##  [46,] -2.869918950  0.36830509
##  [47,] -2.611891601  7.42926492
##  [48,] -3.059042421  7.26360322
##  [49,] -1.441354475  3.18579376
##  [50,] -2.023534796  1.92660501
##  [51,] -1.944332430 -0.13743514
##  [52,] -0.981700942  5.07776480
##  [53,]  1.963346899  8.31291809
##  [54,]  2.885169342  7.92515718
##  [55,]  2.908087574  2.35100292
##  [56,]  0.566552755  3.10971021
##  [57,]  0.397794094  4.12591268
##  [58,] -0.182018529  3.23734941
##  [59,] -0.481870422  5.18144351
##  [60,] -2.073937610  3.83075705
##  [61,] -1.421190326  0.96011827
##  [62,] -8.139571683  3.61965921
##  [63,]  1.251554222  3.54669420
##  [64,] -3.015830554  2.85377886
##  [65,] -1.975396880  1.14465935
##  [66,] -5.238364556  2.62270323
##  [67,] -0.532345946  4.09036254
##  [68,]  0.674465721  9.18864361
##  [69,]  3.013213966 -0.82118883
##  [70,] -5.794006759 -2.08226146
##  [71,] -0.193849328  1.59524003
##  [72,]  1.072784236  0.61520895
##  [73,] -8.064993391 -3.09205262
##  [74,]  0.001184256  2.43611256
##  [75,]  0.849030401  5.20352883
##  [76,] -3.227204289  1.38582167
##  [77,]  0.550290553  3.66168012
##  [78,] -1.030062213  6.69496899
##  [79,] -1.406155568  4.39249993
##  [80,]  0.548423212  2.62275918
##  [81,] -0.941660603  3.99027509
##  [82,] -2.898849730  4.95676378
##  [83,] -1.551536916  1.57456127
##  [84,] -4.277516964  3.84821493
##  [85,] -2.567454903  2.46040420
##  [86,] -3.254276645  0.32371920
##  [87,] -2.866291564  3.95765163
##  [88,] -2.125507814 -7.89354225
##  [89,] -2.910356911 -6.29030715
##  [90,] -2.619577437 -3.25123242
##  [91,] -4.049373606 -1.88764050
##  [92,] -3.723427523  2.34981742
##  [93,]  0.477806544  2.23901853
##  [94,]  2.051133385  1.29897957
##  [95,] -0.727863018  3.83153455
##  [96,] -3.328835686  1.99504946
##  [97,] -2.495301136  1.72912011
##  [98,]  0.095175454  3.72796344
##  [99,] -8.396629089  0.80457223
## [100,]  3.738724514  0.79665331
## [101,] -3.712219749  2.63094175
## [102,] -3.224783823  2.50654745
## [103,] -2.497147912  3.22607252
## [104,] -1.033849219 -0.08842666
## [105,] -1.031783656  1.06827644
## [106,] -0.836162934  1.11236086
## [107,] -2.208370565  1.68265835
## [108,] -1.088435343  2.92184863
## [109,] -0.719433814  3.51114831
## [110,] -4.894302230 -4.57369500
## [111,] -4.526039261 -2.03463402
## [112,] -7.691118112 -5.65077575
## [113,] -3.020503572 -2.16308835
## [114,] -0.123332298  3.59815551
## [115,] -0.198986464  9.37932643
## [116,] -3.312464652  3.67192724
## [117,]  2.668809385  0.50874955
## [118,] -1.055206594  3.52129679
## [119,] -0.198740316  4.89444621
## [120,]  3.580104511  4.14510313
## [121,] -2.910344124  1.05236616
## [122,] -1.596486648  2.57033423
## [123,] -4.691930602 -3.61203767
## [124,] -0.490634941  2.37658133
## [125,] -2.367326469  5.00395716
## [126,]  3.028522092 -4.10372185
## [127,]  2.676558000 -1.33955262
## [128,] -0.256457486  1.09597398
## [129,]  1.541568613  2.61609528
## [130,] -4.730780656 -6.28901773
## [131,] -3.812748747 -5.67830739
## [132,] -3.768087136 -7.85830168
## [133,]  0.052304645  0.04165587
## [134,] -1.653781133  3.89119055
## [135,] -2.173812855  2.76143076
## [136,] -3.129407449 -3.96611542
## [137,] -2.636153649  2.84615866
## [138,] -2.727319668  1.80705964
## [139,]  5.659339881  5.74188603
## [140,]  4.720429245  6.19696333
## [141,]  6.781406218  6.34324558
## [142,]  6.291684502  7.25287261
## [143,]  6.673876190  7.17967627
## [144,]  6.862332189  6.79553344
## [145,]  4.185431867  5.54072897
## [146,] -6.838843962  2.51463304
## [147,]  4.879286292  5.90158096
## [148,]  4.560576909  5.64895709
## [149,]  5.853711981  4.78727630
## [150,]  6.192867409  7.61545141
## [151,]  5.611057028  6.26858418
## [152,]  6.028433065  5.65896210
## [153,]  5.806156525  5.30833550
## [154,]  5.264036479  5.81070840
## [155,] -0.414935170  0.68634929
## [156,] -0.658332848  0.18244971
## [157,]  0.261602098 -0.32903156
## [158,] -0.362892611  2.52259504
## [159,] -0.506497001  0.90748549
## [160,] -1.675555474  3.01724419
## [161,] -1.569810084 -0.70227942
## [162,] -0.645825276  1.49839730
## [163,]  0.233778229  2.00785990
## [164,] -1.200660574  0.65587437
## [165,]  0.936414428  0.88381593
## [166,] -1.413568498  1.28644679
## [167,] -3.865201590 -4.32499255
## [168,] -2.942925668 -7.95358733
## [169,] -4.344287115  0.25540362
## [170,] -1.898791941  1.32746921
## [171,] -2.565207662  0.46769486
## [172,] -2.911097977  1.40308369
## [173,] -3.551461362  3.33418073
## [174,]  0.960033508 -0.27392959
## [175,] -1.636002570  3.62590970
## [176,] -2.201899066  3.30552421
## [177,] -4.881537153  3.90549433
## [178,] -1.459672941  2.38414906
## [179,] -2.911721136  1.98389004
## [180,] -0.226127668  2.09317036
## [181,]  1.832384023  3.22969962
## [182,] -1.224997785  2.60849926
## [183,] -1.982842411  1.70927033
## [184,]  0.207955245  4.50523397
## [185,] -1.363044133  3.63960975
## [186,]  1.332511027  2.00087009
## [187,] -1.000386016  2.44029753
## [188,] -0.014128538  7.56276724
## [189,]  4.481820610 -1.87299732
## [190,]  1.113568772  2.48788261
## [191,] -2.772937296  3.12768841
## [192,]  0.731226194 -1.83927335
## [193,] -3.708222807  7.17962224
## [194,] -1.193278030  7.94327361
## [195,] -4.437682220 -4.27824074
## [196,] -3.193446281 -1.16597922
## [197,] -0.764141723  2.30548265
## [198,] -1.562182712 -0.07418746
## [199,] -3.159556641 -1.89577068
## [200,] -1.198828079  1.40975639
## [201,] -4.890331739  3.36127060
## [202,]  0.186082422  1.59485135
## [203,] -0.562569303  2.78702797
## [204,] -7.592004227 -3.69133874
## [205,] -4.186718566  4.57649282
## [206,] -2.189155256 -1.04421962
## [207,] -1.847541324  0.38399584
## [208,] -0.280496982 -0.91217446
## [209,]  0.155943200  1.23747676
## [210,]  0.486252888  1.27056692
## [211,] -0.279387744 -0.26875735
## [212,] -1.057809991  2.19352022
## [213,] -2.701424327 -0.35733552
## [214,] -2.363729571 -0.51870597
## [215,] -2.018644253  0.73529347
## [216,] -5.506145910  0.94075026
## [217,] -4.289733249 -0.65252402
## [218,] -1.488265003  5.99443830
## [219,] -5.133977447  0.31515243
## [220,] -7.412614129  6.72349026
## [221,] -4.784336822  5.11374650
## [222,] -3.274647942  8.09711794
Ugraph(P0.mRSG.dead.min$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 2, Vcex = .3, prune = T, cut = 0.5, main = "miRNASeqGene data (Deceased Patients)\nFDRcutoff at 1-1e-13, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##               [,1]         [,2]
##   [1,] -0.37990802 -0.593211809
##   [2,] -1.11527186  1.637956620
##   [3,] -5.07997470  1.210418350
##   [4,]  2.47189248 -1.947750370
##   [5,]  3.59990995 -4.212193447
##   [6,]  1.66159111 -2.679772945
##   [7,]  4.18196716 -4.477689119
##   [8,]  0.71499615  0.680191997
##   [9,] -1.29870726  2.055499192
##  [10,] -0.46192156  2.928103602
##  [11,]  5.34582220  0.297960466
##  [12,]  1.67613746  1.076890041
##  [13,] -0.41721288 -1.532773875
##  [14,]  0.06525696 -0.538444357
##  [15,] -0.33465485  0.096904727
##  [16,] -2.45940619  0.606884849
##  [17,]  0.23712022  4.572209840
##  [18,]  0.73539508  2.073928854
##  [19,]  2.04962391  1.775244283
##  [20,] -1.30608368 -0.404025633
##  [21,] -0.25129336  1.044583429
##  [22,]  0.29422801 -0.605443778
##  [23,] -1.46837683 -1.039084066
##  [24,] -1.00013074 -1.192726336
##  [25,]  0.93003441  4.226271112
##  [26,]  2.49150114  3.079798091
##  [27,]  4.40368813  1.207056675
##  [28,]  4.52640041  2.695149845
##  [29,] -0.80819229  2.394709111
##  [30,] -2.48255290  1.543950335
##  [31,]  0.19279516  2.694819688
##  [32,]  0.60265733 -0.635026632
##  [33,] -0.95215013  1.023559558
##  [34,]  0.32075306  0.554039388
##  [35,] -1.78481227 -0.166396891
##  [36,] -0.07225822 -0.146498788
##  [37,] -0.32878372  0.284056048
##  [38,]  0.87665564 -2.101852062
##  [39,]  0.68100041 -1.573502145
##  [40,] -2.88621111 -3.527278937
##  [41,]  3.20545169 -0.356635093
##  [42,]  1.15621820 -7.267641341
##  [43,]  2.84818893  0.422721926
##  [44,]  2.85140819 -0.825306334
##  [45,] -4.66166562  2.138876762
##  [46,] -4.35911530  2.662370096
##  [47,]  0.56819836  6.520767934
##  [48,]  4.15821621  0.683006965
##  [49,] -1.25651529  2.686351141
##  [50,]  1.57455439  1.967601565
##  [51,]  4.48566874 -5.594096718
##  [52,] -0.18296230 -1.220440486
##  [53,]  1.70181433 -1.586869924
##  [54,]  4.80523260  2.249408885
##  [55,]  4.26000580  3.110987270
##  [56,] -1.86727913  6.616056721
##  [57,] -0.17113972  0.399350614
##  [58,] -0.85906823  0.180553779
##  [59,] -3.91865288  0.425137648
##  [60,] -2.96877871 -1.828148025
##  [61,] -0.35718203 -2.349094524
##  [62,] -0.79506255 -1.195507620
##  [63,]  0.94569528 -3.439657011
##  [64,] -3.00347946  0.391600815
##  [65,] -0.70285759  4.274215603
##  [66,]  0.90052143 -1.049182514
##  [67,]  0.63166371 -1.149237340
##  [68,]  3.31434256 -1.795311423
##  [69,] -0.26399703 -0.799606058
##  [70,]  1.37526193  5.249346160
##  [71,]  0.43016855 -3.094043273
##  [72,]  2.38781538  9.805244658
##  [73,] -1.97645505 -2.557986920
##  [74,] -1.37113500 -0.101057952
##  [75,] -6.83718886  1.181522228
##  [76,] -3.40250956 -2.617310131
##  [77,] -1.83867925 -1.071185304
##  [78,] -2.55530844 -0.879424088
##  [79,] -0.12648986 -0.022967726
##  [80,]  1.38216390  0.428025235
##  [81,] -3.78430524  0.031637150
##  [82,] -0.75411060  0.429203923
##  [83,]  1.10654939 -0.632950108
##  [84,] -0.67661930  0.621631216
##  [85,] -0.91033729 -0.167952222
##  [86,]  1.33400286 -0.583368712
##  [87,] -1.67666425 -0.550836695
##  [88,] -6.97268610  2.015361505
##  [89,]  0.98040794  0.674768728
##  [90,]  4.84272183 -5.767564145
##  [91,]  4.62152789 -6.776552580
##  [92,]  3.16560940 -2.221270947
##  [93,]  2.56915555 -5.496765431
##  [94,]  1.70853407  1.333027656
##  [95,]  1.24925489  0.030188356
##  [96,]  2.78333828  1.446812581
##  [97,] -2.03987598 -0.855088087
##  [98,]  3.66357834 -0.927337768
##  [99,]  0.48581421 -0.002767253
## [100,] -2.46730239 -0.211272078
## [101,]  0.95794559 -4.154622554
## [102,]  1.96488002  2.866462239
## [103,] -0.88140936  1.618144076
## [104,] -3.00734124 -0.165740332
## [105,] -0.61583973 -0.862788100
## [106,]  3.01442343 -1.345528458
## [107,]  0.68591377  1.276971075
## [108,]  1.10155440  2.437549562
## [109,]  1.52619478  0.203072082
## [110,] -0.27961688  0.749585154
## [111,] -4.08934990 -4.048493586
## [112,]  5.45487795 -7.760438662
## [113,]  7.95416072 -7.198059108
## [114,]  7.14296055 -8.127177278
## [115,]  6.55759383 -8.542230374
## [116,]  3.04194356 -5.348609764
## [117,]  0.34697651  0.374578029
## [118,] -2.62289500  0.975657736
## [119,] -2.86256418 -6.249786970
## [120,]  6.65433745 -0.265508070
## [121,]  0.40249045 -0.277306853
## [122,] -1.46296767  0.680154606
## [123,] -1.82213888  0.638970875
## [124,]  0.90432868  0.532681314
## [125,] -4.60922339  3.651021298
## [126,]  0.86650996 -0.398117041
## [127,] -1.24455359  0.227989309
## [128,]  8.27792563 -6.622460683
## [129,] -0.68749801  1.343117445
## [130,]  7.57541409 -7.672691203
## [131,]  6.10343545 -7.214913670
## [132,]  6.50257980  1.288949494
## [133,]  2.13050740 -2.451636545
## [134,]  5.18651149 -5.006035139
## [135,] -0.93372359 -3.448314902
## [136,]  6.33876696  2.142397824
## [137,]  0.21853865 -1.103753822
## [138,] -5.05589898 -0.953305925
## [139,]  0.26496199 -7.272713364
## [140,]  3.15214462 -3.765291481
## [141,] -2.09856679 -0.553885074
## [142,]  2.03634025 -0.398698557
## [143,]  0.91737334  1.375443671
## [144,]  3.25519535  5.437593450
## [145,]  5.33805975 -9.379237791
## [146,]  0.44456570  1.145069251
## [147,] -0.64829833  0.102334945
## [148,] -3.32298425  3.966834161
## [149,] -2.75923771  2.327978562
## [150,] -4.39978652  4.585280052
## [151,] -4.27441801  4.325604519
## [152,] -4.01037756  4.543689257
## [153,] -3.71761263  5.099091316
## [154,] -1.98524125  2.655529494
## [155,]  1.03967645  1.656999109
## [156,]  0.30735377  1.651693122
## [157,] -3.92063759  5.453115816
## [158,] -2.22413959  2.475348765
## [159,] -3.11557243  4.321902414
## [160,] -2.54443383  3.568153618
## [161,] -4.16582319  4.901932285
## [162,] -3.79667911  4.807022832
## [163,] -3.39605010  5.073288008
## [164,] -2.74493917  4.184207087
## [165,] -0.98688094 -1.665709303
## [166,] -0.79491381 -2.602241184
## [167,] -0.11192190 -2.405705783
## [168,]  1.14926601 -2.204936809
## [169,] -0.54437297 -2.752995157
## [170,]  0.89236497 -1.773277836
## [171,]  0.17352807 -2.707936810
## [172,]  0.33614523 -2.339790992
## [173,]  0.69310661 -2.457095798
## [174,] -0.12685726 -1.997698914
## [175,] -0.79300413 -2.075455870
## [176,] -0.53863499 -1.330451352
## [177,]  2.55091604 -3.422497827
## [178,]  7.77879559 -3.923421407
## [179,]  1.32079814  2.146150057
## [180,]  0.07039243 -0.003138772
## [181,]  1.27467952  0.707289591
## [182,]  0.40359344  0.779911466
## [183,] -1.83134300  0.146538115
## [184,]  1.96137526  6.294033144
## [185,]  0.05766490  1.642796310
## [186,]  3.48021391  1.437327239
## [187,]  6.94244469  0.810980700
## [188,]  1.95360622  0.360343422
## [189,]  0.18858812 -1.381069152
## [190,] -3.77669691 -1.301920438
## [191,] -4.16343743 -2.762636752
## [192,]  1.68862016 -0.487487222
## [193,] -1.08487860 -0.718321853
## [194,]  1.79551581 -0.014318879
## [195,]  3.27053611 -0.036421690
## [196,]  2.38897807  4.279757776
## [197,] -0.07051155  1.095353445
## [198,]  3.76515436  5.536834170
## [199,]  1.70218482  7.197926378
## [200,] -2.54524366 -1.495033555
## [201,]  2.35269946 -2.529211076
## [202,]  2.77498498 -4.287298432
## [203,]  0.99000918  0.107535315
## [204,]  4.57119615 -1.996029392
## [205,]  3.68541135 -5.014490565
## [206,]  0.66616670  2.406738512
## [207,]  0.87113036 -0.627121000
## [208,]  1.03434042 -1.123748715
## [209,] -2.28465960 -1.795998371
## [210,]  5.90922435  4.275192428
## [211,]  2.86232988  1.968618484
## [212,] -1.60233253  1.551536350
## [213,] -1.18223184  0.404979473
## [214,]  3.94039885 -3.957684880
## [215,] -1.43491593  1.104009792
## [216,]  2.94120380  0.891818267
## [217,]  0.01206979  2.108630984
## [218,] -5.72574004 -2.244830274
## [219,] -5.95146808 -1.666489315
## [220,] -6.24989792 -3.957203226
## [221,]  1.27517682 -3.787627810
## [222,] -0.55014895  1.706671037
## [223,] -1.56467166 -2.342658672
## [224,]  0.68744676  3.319278825
GGM.mRSG.alive.min = as.data.frame(GGMnetworkStats(P0.mRSG.alive.min$sparseParCor, as.table = T))
GGM.mRSG.dead.min = as.data.frame(GGMnetworkStats(P0.mRSG.dead.min$sparseParCor, as.table = T))
GGM.mRSG.alive.min.order = GGM.mRSG.alive.min[order(GGM.mRSG.alive.min$degree, decreasing = T), ]
GGM.mRSG.dead.min.order = GGM.mRSG.dead.min[order(GGM.mRSG.dead.min$degree, decreasing = T), ]

#Output top 5%
GGM.mRSG.alive.min.order[1:round(nrow(GGM.mRSG.alive.min.order) * 0.05), ]
##                degree betweenness   closeness eigenCentrality nNeg nPos
## hsa-mir-206        49   1418.6012 0.002336449       1.0000000   25   24
## hsa-mir-137        43   1165.2189 0.002277904       0.8946383   21   22
## hsa-mir-216b       43   1184.0986 0.002267574       0.7987536   26   17
## hsa-mir-873        39   1203.4487 0.002283105       0.7455373   21   18
## hsa-mir-122        38    695.1543 0.002164502       0.7128671   22   16
## hsa-mir-383        37   1594.7997 0.002262443       0.7025122   20   17
## hsa-mir-9-3        36    728.9325 0.002212389       0.7430145   14   22
## hsa-mir-1258       34   1815.9385 0.002217295       0.5642652   12   22
## hsa-mir-153-1      33    801.5665 0.002207506       0.5658723   13   20
## hsa-mir-1251       32    392.5218 0.002100840       0.6603423   12   20
## hsa-mir-129-2      32    389.5903 0.002132196       0.6670785   15   17
## hsa-mir-187        32    493.6639 0.002207506       0.7099541   12   20
## hsa-mir-135a-2     31    335.6088 0.002092050       0.6254414   17   14
## hsa-mir-3144       31    673.1821 0.002136752       0.6450929   16   15
## hsa-mir-519a-1     31    396.8300 0.002127660       0.6431020   15   16
## hsa-mir-551b       31    436.1201 0.002127660       0.6626297   16   15
## hsa-mir-670        31    449.2775 0.002087683       0.5687669   13   18
## hsa-mir-218-1      30    880.7681 0.002169197       0.5802376   17   13
## hsa-mir-105-2      29   1239.1713 0.002092050       0.4763291   11   18
## hsa-mir-526b       29    470.4592 0.002145923       0.5942396   10   19
## hsa-mir-1270-1     28    261.7844 0.002145923       0.6528327   16   12
## hsa-mir-489        28    495.8287 0.002132196       0.5647228   12   16
## hsa-mir-548b       27    398.1059 0.002164502       0.5635849   15   12
## hsa-mir-552        27    313.4868 0.002109705       0.5898855   13   14
##                mutualInfo variance partialVar
## hsa-mir-206    0.04985656 1.051120          1
## hsa-mir-137    0.04619291 1.047276          1
## hsa-mir-216b   0.03741822 1.038127          1
## hsa-mir-873    0.03962409 1.040420          1
## hsa-mir-122    0.04052999 1.041363          1
## hsa-mir-383    0.04064633 1.041484          1
## hsa-mir-9-3    0.03922892 1.040009          1
## hsa-mir-1258   0.03059035 1.031063          1
## hsa-mir-153-1  0.04363787 1.044604          1
## hsa-mir-1251   0.04218980 1.043092          1
## hsa-mir-129-2  0.02582477 1.026161          1
## hsa-mir-187    0.02790298 1.028296          1
## hsa-mir-135a-2 0.03420975 1.034802          1
## hsa-mir-3144   0.02832720 1.028732          1
## hsa-mir-519a-1 0.03010324 1.030561          1
## hsa-mir-551b   0.03188718 1.032401          1
## hsa-mir-670    0.02609226 1.026436          1
## hsa-mir-218-1  0.03127445 1.031769          1
## hsa-mir-105-2  0.03090548 1.031388          1
## hsa-mir-526b   0.02804850 1.028446          1
## hsa-mir-1270-1 0.02395249 1.024242          1
## hsa-mir-489    0.03185701 1.032370          1
## hsa-mir-548b   0.02280188 1.023064          1
## hsa-mir-552    0.02455820 1.024862          1
GGM.mRSG.dead.min.order[1:round(nrow(GGM.mRSG.dead.min.order) * 0.05), ]
##                degree betweenness   closeness eigenCentrality nNeg nPos
## hsa-mir-499        59   2048.4571 0.002352941       0.9383511   28   31
## hsa-mir-135a-2     54    980.8479 0.002336449       1.0000000   25   29
## hsa-mir-34b        54   1863.7929 0.002398082       0.9225208   27   27
## hsa-mir-122        46    963.1957 0.002267574       0.7693500   26   20
## hsa-mir-3662       46    512.1049 0.002202643       0.8938969   27   19
## hsa-mir-31         44   1223.3786 0.002192982       0.7079726   23   21
## hsa-mir-124-3      43    425.0625 0.002262443       0.8708875   26   17
## hsa-mir-216a       43   1164.1621 0.002293578       0.7490762   21   22
## hsa-mir-383        43    924.8714 0.002247191       0.6978752   26   17
## hsa-mir-3934       41    622.2584 0.002262443       0.7573500   21   20
## hsa-mir-135b       40    398.2131 0.002232143       0.7985608   18   22
## hsa-mir-3690       38    654.6898 0.002232143       0.7037487   20   18
## hsa-mir-1258       37    840.9645 0.002272727       0.6948739   20   17
## hsa-mir-137        37   1689.3834 0.002252252       0.5386194   18   19
## hsa-mir-200a       37    360.3731 0.002164502       0.6872298   16   21
## hsa-mir-206        37    303.2943 0.002123142       0.7436848   12   25
## hsa-mir-548b       37    609.0097 0.002232143       0.6156501   15   22
## hsa-mir-124-2      36    523.4831 0.002222222       0.7269321   20   16
## hsa-mir-1245       35    453.3964 0.002074689       0.5854388   19   16
## hsa-mir-3923       35    816.0978 0.002192982       0.6554986   17   18
## hsa-mir-124-1      34    592.4080 0.002092050       0.6223953   17   17
## hsa-mir-1269       32    214.9781 0.002197802       0.7095134   16   16
## hsa-mir-216b       32    261.3395 0.002123142       0.6741535   14   18
## hsa-mir-3144       32    304.4228 0.002141328       0.5792189   19   13
##                mutualInfo variance partialVar
## hsa-mir-499    0.05698069 1.058635          1
## hsa-mir-135a-2 0.04601150 1.047086          1
## hsa-mir-34b    0.05088074 1.052197          1
## hsa-mir-122    0.03886083 1.039626          1
## hsa-mir-3662   0.03896512 1.039734          1
## hsa-mir-31     0.03849026 1.039241          1
## hsa-mir-124-3  0.03916181 1.039939          1
## hsa-mir-216a   0.03348094 1.034048          1
## hsa-mir-383    0.03646728 1.037140          1
## hsa-mir-3934   0.03847133 1.039221          1
## hsa-mir-135b   0.02800993 1.028406          1
## hsa-mir-3690   0.03196679 1.032483          1
## hsa-mir-1258   0.03038982 1.030856          1
## hsa-mir-137    0.02786437 1.028256          1
## hsa-mir-200a   0.03548027 1.036117          1
## hsa-mir-206    0.03300164 1.033552          1
## hsa-mir-548b   0.02934748 1.029782          1
## hsa-mir-124-2  0.03358706 1.034157          1
## hsa-mir-1245   0.02601377 1.026355          1
## hsa-mir-3923   0.02664374 1.027002          1
## hsa-mir-124-1  0.03165019 1.032156          1
## hsa-mir-1269   0.02672720 1.027088          1
## hsa-mir-216b   0.02458369 1.024888          1
## hsa-mir-3144   0.02583671 1.026173          1
ggplot(GGM.mRSG.alive.min.order, aes(x = reorder(rownames(GGM.mRSG.alive.min.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.mRSG.alive.min.order$degree), linetype = "dashed", color = "red")  +
  # 24th unit: top 5%
  geom_hline(yintercept = GGM.mRSG.alive.min.order[24,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "miRNASeqGene", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-13, Surviving Patients")

ggplot(GGM.mRSG.dead.min.order, aes(x = reorder(rownames(GGM.mRSG.dead.min.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.mRSG.dead.min.order$degree), linetype = "dashed", color = "red")  +
  # 24th unit: top 5%
  geom_hline(yintercept = GGM.mRSG.dead.min.order[24,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "miRNASeqGene", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-13, Deceased Patients")

FDRcut 1-1e-6:

P0.mRSG.alive.6 = sparsify(opt.mRSG.alive$optPrec, threshold = "localFDR", FDRcut=1-1e-6)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  3648 
## - Corresponding to 3.31 % of possible edges 
## 
P0.mRSG.dead.6 = sparsify(opt.mRSG.dead$optPrec, threshold = "localFDR", FDRcut=1-1e-6)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  4048 
## - Corresponding to 3.67 % of possible edges 
## 
set.seed(42)
Ugraph(P0.mRSG.alive.6$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 2, Vcex = .3, prune = T, cut = 0.5, main = "miRNASeqGene data (Surviving Patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##                [,1]         [,2]
##   [1,] -4.410883321  1.575890771
##   [2,]  2.335809123  1.644717176
##   [3,]  0.591960134 -2.445122047
##   [4,] -1.489250356 -3.088565812
##   [5,] -2.915362335  5.295975529
##   [6,]  0.018828042 -0.165810387
##   [7,] -1.668313876 -5.126466482
##   [8,] -1.837556477 -4.683897353
##   [9,]  0.046519743 -4.573038654
##  [10,] -2.056168783 -4.495283720
##  [11,] -0.314511906 -0.802435496
##  [12,] -1.663560317  0.824340517
##  [13,]  0.415990240 -1.598210427
##  [14,] -1.394281427 -2.882330517
##  [15,] -1.299759199 -1.479195299
##  [16,] -1.312404818 -1.873965129
##  [17,] -0.977117451 -1.799296953
##  [18,] -1.180747765 -2.141321710
##  [19,] -4.397224014 -1.031044847
##  [20,]  1.214705864 -3.181072033
##  [21,] -0.845671343 -2.297763020
##  [22,] -3.863637750 -3.692389412
##  [23,] -1.787922426 -2.066947818
##  [24,]  1.415181086 -1.078561361
##  [25,] -2.272174067  2.172629156
##  [26,] -1.679542802  1.481685267
##  [27,]  0.028615243 -0.958979603
##  [28,] -5.982452441 -2.467038117
##  [29,] -0.317161813 -1.023350921
##  [30,] -4.711891834 -8.066507645
##  [31,] -1.049017302 -1.542003183
##  [32,] -1.848478404 -2.853918121
##  [33,] -1.290677767  0.399968962
##  [34,] -1.235513618 -0.008088149
##  [35,] -4.774393731  0.652836006
##  [36,] -2.129528367 -1.106231639
##  [37,] -1.704410659 -1.328018499
##  [38,] -1.526511835 -2.480762671
##  [39,]  0.624411104 -0.745770339
##  [40,] -0.593119680 -1.825318871
##  [41,]  0.156934268 -1.730456735
##  [42,]  1.023015595  3.457763011
##  [43,] -1.898888780 -1.840375006
##  [44,] -4.126708819 -8.017995273
##  [45,] -1.409319362  0.231825795
##  [46,] -1.783167127 -1.586186977
##  [47,]  0.643130475 -1.016550321
##  [48,] -1.474483013 -7.510576898
##  [49,] -1.155727730 -1.039563615
##  [50,] -0.435406423 -1.642519009
##  [51,] -1.230840009 -2.601415346
##  [52,]  2.425190826 -1.747281813
##  [53,]  2.022601999  0.091864034
##  [54,] -0.012648802  5.280941633
##  [55,]  4.226987398  1.984498915
##  [56,]  4.884974247  1.156852365
##  [57,] -4.158247686  1.368235704
##  [58,] -0.787697905 -2.701625771
##  [59,] -0.096300070 -3.078783100
##  [60,] -1.678908659 -6.727704311
##  [61,] -6.861813663 -6.010468121
##  [62,]  5.974604845 -5.077265885
##  [63,]  1.030437241  0.229381328
##  [64,]  0.064431444  0.543478118
##  [65,]  0.512839045 -1.844812106
##  [66,] -1.464072995 -2.135059820
##  [67,] -5.610727884  3.148020138
##  [68,] -0.280719526 -3.209922716
##  [69,]  0.152441843 -0.667507131
##  [70,]  5.017411754  1.972048397
##  [71,]  3.024989617  1.061377749
##  [72,]  2.840239244  1.411534460
##  [73,]  1.871775132 -2.851423754
##  [74,] -1.794552039 -0.060650747
##  [75,] -1.359958863 -0.302257701
##  [76,]  0.578979397  1.246044934
##  [77,]  4.121262936 -6.200403617
##  [78,]  0.112883641 -1.200777477
##  [79,] -0.092283668 -0.659604450
##  [80,] -0.518086431  2.403771667
##  [81,]  0.147284252  0.050280217
##  [82,] -0.586845368 -2.287341333
##  [83,] -0.549335233 -2.417103077
##  [84,]  2.968700540 -3.231405341
##  [85,]  1.187665736 -1.933943693
##  [86,] -0.313243399  3.843220004
##  [87,] -1.617116437 -1.749040049
##  [88,] -0.356000758 -1.851725814
##  [89,] -2.771385449  0.248924187
##  [90,]  0.956021208 -0.870213031
##  [91,]  3.704961513 -2.769025619
##  [92,] -3.287487268  0.691757448
##  [93,]  1.367915524 -3.753348500
##  [94,] -0.404222573 -0.677708554
##  [95,] -2.628234596 -8.147109913
##  [96,]  0.666345296 -3.158733060
##  [97,]  0.294585550  1.487306704
##  [98,] -3.746025574 -2.514182790
##  [99,]  0.047161567 -2.107559891
## [100,]  1.637851373 -0.809673739
## [101,] -1.252444440 -1.341469856
## [102,]  0.716679124 -1.357676237
## [103,]  0.835233128 -0.314140892
## [104,] -0.742176081 -3.243965090
## [105,]  0.282714293 -2.614472083
## [106,] -0.736216004 -0.415007759
## [107,] -2.422508904 -1.025411931
## [108,] -1.438157032 -1.447334811
## [109,]  0.117134680 -3.285461049
## [110,] -1.091163696 -2.231508979
## [111,] -4.887688811 -2.615459604
## [112,] -0.448915993 -3.143431244
## [113,] -0.197722782 -2.419963727
## [114,] -2.369663685 -6.576384039
## [115,] -2.910961256 -6.220556071
## [116,]  1.774412146 -4.781130398
## [117,] -2.112672626 -5.130001913
## [118,] -1.254956771 -4.534439825
## [119,]  1.648170864  2.378697666
## [120,] -7.541990659  0.301785911
## [121,] -1.186587779 -0.643912601
## [122,]  0.966339129 -1.737774583
## [123,]  2.896453576 -0.474965784
## [124,]  0.532757347 -2.669035175
## [125,] -0.584605858 -2.983460856
## [126,] -0.494878660 -2.095808631
## [127,]  6.419205404  1.262017536
## [128,]  1.044319149 -1.997908760
## [129,]  0.914374304  0.520646191
## [130,]  0.518028013 -4.036117121
## [131,] -1.631312056 -1.145501924
## [132,] -2.031222754 -2.166930049
## [133,] -1.364297871 -0.992578158
## [134,] -0.489157656 -2.636289844
## [135,] -0.048702709 -1.808417324
## [136,] -1.257625569 -3.048733044
## [137,]  0.023376565 -1.683726165
## [138,]  1.302730212 -9.682032310
## [139,] -0.223284057 -1.995209906
## [140,] -3.020928565 -7.035526352
## [141,] -0.376626826 -1.409632899
## [142,] -5.241578477 -0.699286533
## [143,] -0.648501239 -5.723321914
## [144,] -2.303581394 -4.032290700
## [145,] -0.925664359 -7.358354438
## [146,] -2.486999056 -9.340951045
## [147,] -3.646313151 -4.417066853
## [148,] -1.337974474 -8.639685975
## [149,] -2.728174458 -4.333499074
## [150,] -1.896500647 -8.122664460
## [151,] -4.731306142 -6.692854250
## [152,]  0.728307313 -1.689116673
## [153,] -3.291973009  0.100443887
## [154,]  1.819588216  4.894978725
## [155,] -0.944008479 -1.987205566
## [156,]  2.382959109 -3.649334092
## [157,]  0.382620882 -1.071924599
## [158,]  1.009418179  1.297912043
## [159,]  1.077164409 -1.255289166
## [160,]  1.872414898 -2.334490697
## [161,] -1.949216327 -0.926125583
## [162,]  0.139925363 -2.212225086
## [163,] -2.519093963 -7.746637998
## [164,] -3.873351847 -7.140992973
## [165,] -2.350543754 -5.342256561
## [166,] -0.681510578 -1.985570812
## [167,] -5.058532526 -5.090354125
## [168,] -4.260944809 -6.625325469
## [169,] -2.152362754  0.023001289
## [170,] -4.078465593 -5.046213648
## [171,]  0.529051339  5.772529383
## [172,] -3.562032809  2.207608676
## [173,]  5.449755088 -5.932405566
## [174,]  0.258881337 -0.861075609
## [175,]  1.558190676 -1.529528768
## [176,] -3.196025454 -6.117098150
## [177,]  1.135069674  5.655357464
## [178,] -3.067658817 -5.342132342
## [179,] -2.525644824 -6.905679491
## [180,] -1.710317322 -1.686871995
## [181,]  0.770053246 -1.115532786
## [182,] -0.889466565 -1.115339770
## [183,] -6.411060931 -4.071315556
## [184,] -3.452797741 -5.670169892
## [185,] -1.984916938 -7.236165605
## [186,] -3.948842062 -6.772132548
## [187,]  3.286024847 -4.431134103
## [188,] -0.871478025 -2.966618795
## [189,]  2.398014797  3.130548679
## [190,] -0.457917743 -1.093790648
## [191,]  4.874403628 -0.823545321
## [192,]  3.149587347 -1.574723880
## [193,]  4.943793524 -1.118288685
## [194,]  4.931058238 -1.823442414
## [195,]  4.853772496 -1.482084579
## [196,]  5.075907919 -1.389939855
## [197,]  3.004265753 -1.356291136
## [198,]  3.944404100  0.626683195
## [199,]  0.215191629  0.844721929
## [200,]  3.108484585 -1.202179660
## [201,]  3.253644446 -1.282548041
## [202,]  4.847867693 -0.567656090
## [203,]  4.456639455 -1.554325222
## [204,]  4.373812357 -0.793421824
## [205,]  4.439442976 -1.122033048
## [206,]  4.493242341 -1.808078100
## [207,]  3.146301940 -1.777766781
## [208,] -2.576945671 -1.654570810
## [209,] -2.338601342 -1.976677896
## [210,] -2.494241675 -2.256383464
## [211,] -1.193917391 -1.647399828
## [212,] -1.047698311 -3.041849850
## [213,] -0.280649691 -2.672057043
## [214,] -1.523627975 -2.702669362
## [215,] -1.979321058 -1.307195674
## [216,] -2.295173088 -1.500246618
## [217,] -2.360214573 -2.754907539
## [218,] -2.929545887 -2.748930195
## [219,] -1.040939331 -2.460273904
## [220,] -3.315826238 -7.724123688
## [221,] -3.102881269 -4.073214802
## [222,] -4.464880419 -6.124423029
## [223,]  0.488248853 -3.528164692
## [224,] -6.136323236 -1.357393703
## [225,] -0.616482742 -1.109353344
## [226,] -1.131395516 -2.744216017
## [227,] -0.080778885 -1.120145770
## [228,] -2.495087822 -0.562719539
## [229,] -0.409583496  0.169814004
## [230,] -5.013497076 -1.239710036
## [231,] -0.201000349 -1.675653209
## [232,] -0.659776150 -1.217635517
## [233,] -2.093712231 -0.395424586
## [234,] -1.052911698 -1.310619302
## [235,] -0.126442654 -2.182047178
## [236,] -0.865611754 -0.708255088
## [237,] -1.624120721 -2.291762155
## [238,]  4.322357637  3.640571295
## [239,] -1.795067718 -2.380295850
## [240,] -0.884916967 -2.583485674
## [241,] -4.676290859  3.935273362
## [242,]  1.234198139 -2.206649881
## [243,] -1.344275446 -0.781834221
## [244,] -1.636640083 -0.044271607
## [245,] -0.407213638 -2.178395768
## [246,] -3.276562121  1.480295698
## [247,]  1.620761102  0.825794715
## [248,]  0.381681629 -0.305780699
## [249,] -2.070331379 -1.748017500
## [250,] -0.826346481  0.576633297
## [251,] -0.109201830  1.004898660
## [252,]  1.512920162  0.035661241
## [253,] -1.704500974 -9.359557023
## [254,] -2.687025086 -5.136098585
## [255,] -1.373852211 -4.768538012
## [256,]  0.615020400 -2.045108778
## [257,] -0.648733122 -4.223630401
## [258,] -2.630604850 -4.631170305
## [259,] -0.681233989 -1.600154955
## [260,] -2.623771908 -1.225437998
## [261,] -0.053342742 -1.448642437
## [262,] -0.507464135 -0.269503764
## [263,]  2.444482445 -4.447521842
## [264,] -3.737967911 -8.246254767
## [265,] -3.091057488 -1.674329033
## [266,] -0.534998523 -0.440083312
## [267,] -0.004804377 -2.611609816
## [268,] -1.796875108 -4.168109507
## [269,] -1.022089025 -0.375944870
## [270,] -0.917561681 -0.468124895
## [271,] -1.719139179 -0.776545153
## [272,] -3.445036893 -7.131718064
## [273,] -0.869957808 -0.996888749
## [274,] -2.244719713 -2.463093322
## [275,] -2.086957126 -2.721425423
## [276,] -1.376203399 -2.234149195
## [277,]  2.725104826 -2.820393516
## [278,] -0.697821035 -3.655634478
## [279,]  0.887098216 -2.872899438
## [280,] -3.046523696 -2.014385259
## [281,]  0.379640414 -3.792495488
## [282,] -0.916271443  1.142037047
## [283,] -0.484373014  0.967978088
## [284,] -2.444900753  3.071073093
Ugraph(P0.mRSG.dead.6$sparseParCor, type = "fancy", lay = "layout_with_fr", Vsize = 2, Vcex = .3, prune = T, cut = 0.5, main = "miRNASeqGene data (Deceased Patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##                [,1]         [,2]
##   [1,]  1.281236418  5.106468072
##   [2,] -6.478105951  2.156015776
##   [3,] -6.275597006 -8.518184316
##   [4,] -3.014133446  1.039861088
##   [5,] -0.611527829 -2.731583729
##   [6,] -0.732168798 -3.672453237
##   [7,]  1.340166125  2.361472052
##   [8,] -1.991866649 -3.873557757
##   [9,]  1.428444088 -2.254433332
##  [10,]  3.224289437 -1.936666095
##  [11,]  1.121340150 -1.325353626
##  [12,]  2.153955350 -1.957202462
##  [13,] -1.376813123 -3.197361564
##  [14,] -0.127915632 -3.088871567
##  [15,] -2.042434749 -1.408432552
##  [16,] -2.393752403 -5.207143010
##  [17,] -2.534858234 -4.766402741
##  [18,] -2.390092852 -2.183436105
##  [19,] -1.888887915 -2.050897725
##  [20,] -1.409484636 -2.563546566
##  [21,] -1.240036946 -1.937070898
##  [22,] -0.207856680 -2.791011977
##  [23,] -2.893408068 -0.330558034
##  [24,] -2.654727270 -1.459732684
##  [25,] -1.265352183 -3.456141666
##  [26,] -0.990909193 -1.282256021
##  [27,] -1.750973184 -2.328082156
##  [28,] -1.239732468  3.362150205
##  [29,]  0.405720725  5.352233054
##  [30,] -0.700214324 -2.376334075
##  [31,]  5.901657906 -1.407372708
##  [32,] -1.496205672 -0.861666885
##  [33,] -1.086278616 -1.542826885
##  [34,] -1.284479442 -4.526485891
##  [35,] -8.139021031  0.165509817
##  [36,] -4.459206231 -3.341462717
##  [37,] -3.875792489 -1.399798819
##  [38,] -4.330123960 -2.171704407
##  [39,] -3.021033676 -1.448238154
##  [40,] -1.686889853 -0.709593311
##  [41,] -3.126170054 -2.309302683
##  [42,] -0.283898698 -3.497920037
##  [43,] -2.542309311 -2.514492868
##  [44,] -1.017084663 -2.358038903
##  [45,]  3.730458456 -3.447570187
##  [46,] -0.559640415 -3.404401604
##  [47,] -1.730409577 -1.915640125
##  [48,] -2.291364432 -2.051317363
##  [49,]  5.820122117 -2.029189569
##  [50,] -0.053743237 -2.492731629
##  [51,]  0.169721016 -2.652958317
##  [52,]  0.386844165 -3.581957367
##  [53,] -0.311608241 -0.784950831
##  [54,] -0.262104062  1.600376287
##  [55,] -3.499298480  0.178234922
##  [56,]  2.687478688 -6.241841279
##  [57,] -4.767795160 -2.035341562
##  [58,] -4.423002058 -9.434536343
##  [59,] -1.301153432 -1.150387548
##  [60,] -0.410958656 -1.328769618
##  [61,]  5.678408720 -2.513636087
##  [62,] -4.090689054 -3.250133080
##  [63,] -4.997035436 -3.474410308
##  [64,] -0.676328383 -6.215106364
##  [65,] -1.859596884 -6.042117785
##  [66,] -2.923730935 -1.128015973
##  [67,] -1.084049103 -3.943575606
##  [68,]  3.232935499 -5.487081617
##  [69,] -0.429720178 -1.732300412
##  [70,] -1.332772515 -3.629615396
##  [71,] -1.287572761 -9.463462343
##  [72,] -4.280248532 -2.656010371
##  [73,] -3.980410606 -2.642889063
##  [74,] -5.895268776 -4.506705033
##  [75,] -5.230061290 -5.600258670
##  [76,] -5.578178762 -5.094846798
##  [77,] -1.988584928 -2.572052877
##  [78,] -2.053609935 -1.985463183
##  [79,] -1.489898433 -3.562357208
##  [80,]  0.039040015 -1.265056319
##  [81,] -1.849447038 -6.560314040
##  [82,]  0.780131993 -3.262010724
##  [83,] -0.034635430 -1.718216989
##  [84,] -7.171005677 -6.640298838
##  [85,]  0.484053725 -2.907343955
##  [86,]  1.760857353  4.255979196
##  [87,] -0.717254988 -4.282206582
##  [88,]  0.880230129 -2.302948754
##  [89,] -6.253383706 -2.106107623
##  [90,] -0.674238523 -3.081518064
##  [91,] -0.367164252 -3.020389706
##  [92,]  0.234409613 -0.420993683
##  [93,] -1.417386180 -1.633906940
##  [94,] -2.498927695  1.406197358
##  [95,] -1.071029411 -5.222423710
##  [96,] -4.195104725 -7.488946652
##  [97,]  0.513453877 -1.410471726
##  [98,] -1.300621284 -5.633451481
##  [99,] -1.773199135 -0.298638319
## [100,] -0.869363382 -1.936304682
## [101,]  4.917067828 -3.314341789
## [102,] -2.854531073 -8.037852725
## [103,] -3.504079886 -7.784745787
## [104,] -3.769042204 -5.184306211
## [105,] -1.402804994  4.822142775
## [106,] -6.907341185 -7.839535367
## [107,] -0.103770025 -0.965126338
## [108,] -0.566097033 -2.500272079
## [109,] -1.982178253 -4.340208171
## [110,] -1.197829305 -1.609677054
## [111,] -1.925089902 -3.040016886
## [112,] -0.274436451 -0.518030546
## [113,] -0.763955467 -2.143048885
## [114,] -0.735196388 -0.565905690
## [115,] -1.491638531 -2.257742781
## [116,] -1.335180009 -1.834426199
## [117,] -0.915029487 -3.310673864
## [118,] -0.807612098 -1.678688794
## [119,]  2.708111617 -8.408004766
## [120,] -1.590131339 -4.728751906
## [121,] -1.756208101 -1.702809204
## [122,]  3.718073484 -1.090197225
## [123,]  2.796696408 -0.944720078
## [124,]  1.798689848 -1.800915826
## [125,]  1.786463742 -0.993869588
## [126,] -0.940717704 -7.354279315
## [127,] -6.570707588  0.534853867
## [128,]  3.115920493 -7.873670726
## [129,] -2.636852378 -2.845152626
## [130,] -0.836580749 -1.255090282
## [131,] -1.491333614  0.295457785
## [132,] -0.490475597  4.641088781
## [133,] -6.275854505 -0.124934158
## [134,] -3.187564493 -2.622873885
## [135,] -2.960487817 -2.906721355
## [136,] -0.239123698 -3.787071880
## [137,] -1.796671723 -4.267733526
## [138,]  0.665968890 -7.204905336
## [139,] -2.513521787 -0.060702582
## [140,] -2.446344705 -4.303520452
## [141,] -0.763631087 -3.996999026
## [142,] -1.972276297 -2.763089542
## [143,] -2.039952888 -2.273471359
## [144,] -2.518988985  2.359531683
## [145,] -4.813918905 -7.334903084
## [146,]  0.140893478 -3.824199495
## [147,] -2.595921128 -1.745084143
## [148,] -0.571586410 -4.046408584
## [149,]  4.941458323 -1.690796162
## [150,] -2.606236218 -3.048412355
## [151,]  4.542867173 -1.822108653
## [152,] -0.983262345 -2.050266247
## [153,]  0.840993342  0.987864008
## [154,]  4.228563715 -1.426143650
## [155,]  2.300634630 -0.666161855
## [156,]  3.494340674 -0.387266102
## [157,]  3.524025658  0.069473391
## [158,]  5.130482138 -2.009042192
## [159,] -5.997651075 -3.681026041
## [160,]  4.046579393 -3.356954190
## [161,]  2.032401003 -1.447083748
## [162,]  2.799678102  0.557562916
## [163,]  4.482518490 -0.528382613
## [164,] -1.777040850 -2.692331570
## [165,] -1.189050319 -2.497636889
## [166,]  0.594684322 -4.914614497
## [167,]  0.604748752  4.536581516
## [168,] -3.890604441 -4.588571097
## [169,] -1.292937505 -2.201387844
## [170,] -2.465356657 -3.675274174
## [171,] -0.737774819 -2.905232945
## [172,] -1.506464500 -3.209235402
## [173,] -3.051522892 -3.782448816
## [174,] -1.378976886 -2.051676878
## [175,] -3.296473971 -2.949265937
## [176,]  4.432211991 -3.059583783
## [177,]  4.166026394 -2.182495267
## [178,]  5.304195771 -1.333193300
## [179,]  1.754915563  0.005813276
## [180,] -8.212312920 -1.195945422
## [181,] -2.520257428 -2.306386595
## [182,]  3.879662917 -2.682853096
## [183,]  3.827440972 -2.366999924
## [184,]  1.961600243  2.113562006
## [185,]  0.891796704 -1.781589435
## [186,]  1.177696200 -5.000580010
## [187,]  0.577873799 -0.820072419
## [188,] -4.179737904 -3.629812773
## [189,] -0.115230711 -2.294600194
## [190,] -2.813616584 -4.484625895
## [191,]  4.783080130 -2.214439428
## [192,] -0.437606166  0.522921007
## [193,]  2.609792973 -2.136544991
## [194,]  5.045677769 -2.650378619
## [195,] -1.026311132 -3.738922761
## [196,] -2.015629368 -2.443574560
## [197,] -1.691614530 -2.822038112
## [198,]  2.378304931  1.396396947
## [199,]  4.997737872 -1.186512418
## [200,]  0.588959581 -5.769720233
## [201,]  3.898401641 -1.647820762
## [202,] -2.152196546 -3.007796746
## [203,] -5.702742240 -8.065562665
## [204,] -7.868116974 -5.295119068
## [205,] -3.733276270  3.736794822
## [206,] -2.136142982 -1.763692295
## [207,] -3.250810152 -0.702632399
## [208,] -2.674630188 -2.002712295
## [209,] -4.237012264 -0.179146291
## [210,] -4.271704805 -0.597891399
## [211,] -3.894191804 -0.409637170
## [212,] -3.905195753 -0.132374867
## [213,] -2.488131284 -1.390038905
## [214,] -2.435531385 -2.822973618
## [215,] -2.958133544 -2.267126190
## [216,] -4.770280594 -0.190443349
## [217,] -2.283440250 -0.992250525
## [218,] -3.926677857 -1.015974304
## [219,] -3.550439906 -0.878367241
## [220,] -4.319698013 -1.143570867
## [221,] -4.564034288 -1.191416668
## [222,] -4.752691409 -0.914579213
## [223,] -3.313498854 -0.430458476
## [224,] -0.919944155 -2.768806988
## [225,] -0.006442715 -3.524269899
## [226,]  0.445706119 -3.237776502
## [227,]  0.484399325 -2.476188055
## [228,]  0.139709396 -3.350348750
## [229,] -0.395489721 -2.453663240
## [230,]  0.435173133 -2.170610106
## [231,]  0.152956079 -2.007080894
## [232,]  0.582098670 -2.692861295
## [233,] -0.264274704 -1.910613387
## [234,] -1.070670209 -2.974383168
## [235,] -0.269808917 -2.255863198
## [236,]  4.749720232 -0.983485269
## [237,]  1.764982932 -2.429642539
## [238,]  4.226706458 -0.759273579
## [239,] -2.940034206 -1.821094023
## [240,] -1.607274211 -1.786388999
## [241,] -2.051463996 -3.158804470
## [242,] -1.777741184 -3.636534144
## [243,] -2.774902268 -3.178140123
## [244,] -3.066189665 -4.656366870
## [245,] -2.209115714 -3.778960210
## [246,] -3.423451002 -4.492136474
## [247,] -3.133266985 -5.101465995
## [248,] -1.799952477 -3.091962473
## [249,] -0.902294808 -2.999860526
## [250,] -2.794087450 -3.981702417
## [251,] -3.482825317 -3.540849343
## [252,] -1.284158273 -0.073873357
## [253,] -2.565327112  4.975166381
## [254,] -1.788659158 -1.418481960
## [255,] -1.198270041 -2.729332300
## [256,] -1.119602298 -0.967577294
## [257,] -0.931628685 -4.942092635
## [258,] -4.487856017 -4.826665687
## [259,] -2.336797048 -2.357847858
## [260,] -4.302388030 -4.157214738
## [261,] -8.714075433 -3.410142518
## [262,] -1.916482777 -5.515516856
## [263,] -2.434464357 -3.348664719
## [264,]  3.992132830  2.984633042
## [265,] -5.990197538  2.652841304
## [266,] -1.103994137  1.750778390
## [267,]  5.576208175 -0.472309842
## [268,]  4.602446199 -2.816606187
## [269,]  1.696885920 -2.254014792
## [270,] -0.607982630 -0.314318833
## [271,] -0.385922804 -2.699039133
## [272,] -0.921477055 -0.257947859
## [273,]  2.746191046 -1.973818970
## [274,] -1.281717607 -4.051686399
## [275,] -0.557282797 -1.970509863
## [276,] -0.722446870 -3.221349529
## [277,] -1.635133906 -3.937724710
## [278,] -4.076137380  0.817756692
## [279,]  4.097682324 -0.260194914
## [280,] -2.190904363 -0.488484274
## [281,] -1.445675198 -4.250167626
## [282,] -1.280286551 -3.042256313
## [283,]  3.251371797 -1.595884741
## [284,] -3.411395580 -1.880547486
## [285,] -2.074012374 -1.048844773
## [286,] -1.763275223 -3.393683940
## [287,] -8.031295007 -5.947837585
## [288,]  4.552575074 -2.467472528
## [289,]  0.726705272 -4.141446091
## [290,] -0.633027134 -4.603009199
## [291,] -0.259343888 -4.438026599
## [292,]  0.179184260 -4.812754645
## [293,] -4.726005771  1.494911534
## [294,] -1.553497203 -1.140751536
## [295,] -2.220543169 -1.580859432
## [296,] -1.130912306 -0.703701031
## [297,] -0.445929915 -4.860896210
## [298,] -8.533611002 -4.595137311
## [299,] -0.757860775 -6.771858128
## [300,] -0.521013782  5.471258471
GGM.mRSG.alive.6 = as.data.frame(GGMnetworkStats(P0.mRSG.alive.6$sparseParCor, as.table = T))
GGM.mRSG.dead.6 = as.data.frame(GGMnetworkStats(P0.mRSG.dead.6$sparseParCor, as.table = T))
GGM.mRSG.alive.6.order = GGM.mRSG.alive.6[order(GGM.mRSG.alive.6$degree, decreasing = T), ]
GGM.mRSG.dead.6.order = GGM.mRSG.dead.6[order(GGM.mRSG.dead.6$degree, decreasing = T), ]

#Output top 5%
GGM.mRSG.alive.6.order[1:round(nrow(GGM.mRSG.alive.6.order) * 0.05), ]
##                degree betweenness   closeness eigenCentrality nNeg nPos
## hsa-mir-137        87   1837.7076 0.001992032       1.0000000   48   39
## hsa-mir-383        84   1992.8389 0.002004008       0.9541137   44   40
## hsa-mir-218-1      76   1669.6722 0.001923077       0.7818418   44   32
## hsa-mir-206        74   1012.6948 0.001984127       0.8844179   39   35
## hsa-mir-216b       74   1225.0672 0.001980198       0.8412584   46   28
## hsa-mir-873        73   1313.1597 0.001915709       0.8216630   38   35
## hsa-mir-122        67   1451.5418 0.001912046       0.7415744   33   34
## hsa-mir-489        67   1111.2468 0.001915709       0.8137188   28   39
## hsa-mir-1251       65    395.6778 0.001919386       0.8774891   29   36
## hsa-mir-488        64   1180.6475 0.001926782       0.7163433   34   30
## hsa-mir-135a-2     63    810.8663 0.001923077       0.7851301   33   30
## hsa-mir-329-1      63   1899.6925 0.001811594       0.3850826   15   48
## hsa-mir-1258       61   1211.1747 0.001869159       0.6216818   27   34
## hsa-mir-3144       61    515.1572 0.001883239       0.7482783   30   31
## hsa-mir-3923       60    627.8127 0.001834862       0.7778098   29   31
## hsa-mir-551b       60    403.3193 0.001912046       0.7745075   32   28
## hsa-mir-670        59    382.6725 0.001862197       0.7336644   33   26
## hsa-mir-618        58    365.6687 0.001831502       0.7536765   30   28
## hsa-mir-767        58    482.9527 0.001901141       0.7356146   30   28
## hsa-mir-153-1      57    379.2604 0.001886792       0.6984854   28   29
## hsa-mir-105-1      56   1017.8826 0.001841621       0.6306196   24   32
## hsa-mir-105-2      56    771.4176 0.001845018       0.6404011   26   30
## hsa-mir-1305       56    424.6814 0.001869159       0.6788947   28   28
## hsa-mir-187        56    333.7653 0.001855288       0.7495519   23   33
##                mutualInfo variance partialVar
## hsa-mir-137    0.05792767 1.059638          1
## hsa-mir-383    0.05465824 1.056180          1
## hsa-mir-218-1  0.04385892 1.044835          1
## hsa-mir-206    0.05367832 1.055145          1
## hsa-mir-216b   0.04699794 1.048120          1
## hsa-mir-873    0.04789463 1.049060          1
## hsa-mir-122    0.04895314 1.050171          1
## hsa-mir-489    0.04195829 1.042851          1
## hsa-mir-1251   0.04952727 1.050774          1
## hsa-mir-488    0.02966610 1.030111          1
## hsa-mir-135a-2 0.04266945 1.043593          1
## hsa-mir-329-1  0.02971638 1.030162          1
## hsa-mir-1258   0.03681363 1.037500          1
## hsa-mir-3144   0.03689478 1.037584          1
## hsa-mir-3923   0.03681679 1.037503          1
## hsa-mir-551b   0.03823331 1.038974          1
## hsa-mir-670    0.03372321 1.034298          1
## hsa-mir-618    0.03482732 1.035441          1
## hsa-mir-767    0.04426712 1.045262          1
## hsa-mir-153-1  0.04813209 1.049309          1
## hsa-mir-105-1  0.03794908 1.038678          1
## hsa-mir-105-2  0.03916218 1.039939          1
## hsa-mir-1305   0.03149700 1.031998          1
## hsa-mir-187    0.03412042 1.034709          1
GGM.mRSG.dead.6.order[1:round(nrow(GGM.mRSG.dead.6.order) * 0.05), ]
##                degree betweenness   closeness eigenCentrality nNeg nPos
## hsa-mir-499       101   2695.7817 0.001937984       0.9935124   51   50
## hsa-mir-122        95   1935.1052 0.001908397       1.0000000   49   46
## hsa-mir-34b        87   2349.9015 0.001915709       0.9490625   41   46
## hsa-mir-135a-2     85   1112.3558 0.001811594       0.9889366   43   42
## hsa-mir-137        79   2100.1161 0.001862197       0.7916283   38   41
## hsa-mir-383        78    779.5652 0.001811594       0.8295444   50   28
## hsa-mir-3923       76   1658.6989 0.001838235       0.8465114   38   38
## hsa-mir-1258       75    785.0977 0.001763668       0.8618237   41   34
## hsa-mir-3662       75    952.5645 0.001766784       0.8837260   39   36
## hsa-mir-216a       74   1268.1457 0.001865672       0.8135543   38   36
## hsa-mir-31         72    544.9517 0.001792115       0.8325622   40   32
## hsa-mir-124-3      70    598.5971 0.001821494       0.8612900   33   37
## hsa-mir-200a       70    766.1566 0.001828154       0.8150167   32   38
## hsa-mir-1245       68    686.8029 0.001808318       0.8118720   34   34
## hsa-mir-577        68   1076.6221 0.001848429       0.7798470   32   36
## hsa-mir-1197       66   1448.1652 0.001712329       0.4119729   17   49
## hsa-mir-3166       66    585.9955 0.001814882       0.7648800   27   39
## hsa-mir-429        66    763.7550 0.001730104       0.7210737   33   33
## hsa-mir-548b       66    572.3429 0.001811594       0.7223032   27   39
## hsa-mir-346        65    817.6931 0.001733102       0.7394082   28   37
## hsa-mir-3690       65   1083.3959 0.001828154       0.7653713   40   25
## hsa-mir-3934       65    497.7596 0.001831502       0.8112931   32   33
## hsa-mir-124-2      64    801.2065 0.001785714       0.7727001   34   30
## hsa-mir-1269       64    497.1907 0.001818182       0.8355177   27   37
##                mutualInfo variance partialVar
## hsa-mir-499    0.06262338 1.064626          1
## hsa-mir-122    0.04895106 1.050169          1
## hsa-mir-34b    0.05565476 1.057233          1
## hsa-mir-135a-2 0.04909062 1.050316          1
## hsa-mir-137    0.03605993 1.036718          1
## hsa-mir-383    0.04222177 1.043126          1
## hsa-mir-3923   0.03482351 1.035437          1
## hsa-mir-1258   0.03729357 1.037998          1
## hsa-mir-3662   0.04284384 1.043775          1
## hsa-mir-216a   0.03886051 1.039625          1
## hsa-mir-31     0.04139258 1.042261          1
## hsa-mir-124-3  0.04291929 1.043854          1
## hsa-mir-200a   0.04055197 1.041385          1
## hsa-mir-1245   0.03116445 1.031655          1
## hsa-mir-577    0.03198705 1.032504          1
## hsa-mir-1197   0.02451625 1.024819          1
## hsa-mir-3166   0.03335668 1.033919          1
## hsa-mir-429    0.03063146 1.031105          1
## hsa-mir-548b   0.03433014 1.034926          1
## hsa-mir-346    0.03234074 1.032869          1
## hsa-mir-3690   0.03605706 1.036715          1
## hsa-mir-3934   0.04097539 1.041826          1
## hsa-mir-124-2  0.03843682 1.039185          1
## hsa-mir-1269   0.03232673 1.032855          1
ggplot(GGM.mRSG.alive.6.order, aes(x = reorder(rownames(GGM.mRSG.alive.6.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.mRSG.alive.6.order$degree), linetype = "dashed", color = "red")  +
  # 24th unit: top 5%
  geom_hline(yintercept = GGM.mRSG.alive.6.order[24,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "miRNASeqGene", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-6, Surviving Patients")

ggplot(GGM.mRSG.dead.6.order, aes(x = reorder(rownames(GGM.mRSG.dead.6.order), -degree), y = degree)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.mRSG.dead.6.order$degree), linetype = "dashed", color = "red")  +
  # 24th unit: top 5%
  geom_hline(yintercept = GGM.mRSG.dead.6.order[24,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "miRNASeqGene", guide = guide_axis(angle = 90)) +
  ggtitle("Variables sorted by degree, FDR = 1-1e-6, Deceased Patients")

D. All Data

data.all.alive = data.numeric[which(data.Y == 0), ]
data.all.dead = data.numeric[which(data.Y == 1), ]
set.seed(42)
opt.all.alive = optPenalty.kCVauto(Y = data.all.alive, lambdaMin = 1e-11, lambdaMax = 10)
opt.all.alive$optLambda
## [1] 1.148033
set.seed(42)

#Warning: NA/Inf replaced by maximum positive value, lambda stuck at 1000
#Same as above, solved by setting lambdaMax = 10 instead of 1000

opt.all.dead = optPenalty.kCVauto(Y = data.all.dead, lambdaMin = 1e-11, lambdaMax = 10)
#opt.all.dead.10 = optPenalty.kCVauto(Y = data.all.dead, lambdaMin = 1e-11, lambdaMax = 1000, fold = 10)
#opt.all.dead.5 = optPenalty.kCVauto(Y = data.all.dead, lambdaMin = 1e-11, lambdaMax = 1000, fold = 5)
#opt.all.dead.3 = optPenalty.kCVauto(Y = data.all.dead, lambdaMin = 1e-11, lambdaMax = 1000, fold = 3)
#setNames(c(3,5,10,43), c(opt.all.dead.3$optLambda, opt.all.dead.5$optLambda, opt.all.dead.10$optLambda, opt.all.dead$optLambda))
opt.all.dead$optLambda
## [1] 0.976131
edgeHeat(opt.all.alive$optPrec, diag = F, textsize = 1)
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

edgeHeat(opt.all.dead$optPrec, diag = F, textsize = 1)
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

Smallest possible FDRcut:

P0.all.alive.min = sparsify(opt.all.alive$optPrec, threshold = "localFDR", FDRcut=1-1e-14)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  5712 
## - Corresponding to 2.33 % of possible edges 
## 
P0.all.dead.min = sparsify(opt.all.dead$optPrec, threshold = "localFDR", FDRcut=1-1e-14)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  6334 
## - Corresponding to 2.58 % of possible edges 
## 
PcorP.a.min = pruneMatrix(P0.all.alive.min$sparseParCor)
Colors.a.min <- rownames(PcorP.a.min)
Colors.a.min[grep("hsa", rownames(PcorP.a.min))] <- "red"
Colors.a.min[grep(".RPPA", rownames(PcorP.a.min))] <- "green"
Colors.a.min[grep(".R2Gn", rownames(PcorP.a.min))] <- "cyan"
PcorP.d.min = pruneMatrix(P0.all.dead.min$sparseParCor)
Colors.d.min <- rownames(PcorP.d.min)
Colors.d.min[grep("hsa", rownames(PcorP.d.min))] <- "red"
Colors.d.min[grep(".RPPA", rownames(PcorP.d.min))] <- "green"
Colors.d.min[grep(".R2Gn", rownames(PcorP.d.min))] <- "cyan"
set.seed(42)
Ugraph(PcorP.a.min, type = "fancy", lay = "layout_with_fr", Vsize = 2, Vcex = .3, prune = T, cut = 0.5,
       Vcolor = Colors.a.min,
       main = "All Numerical data (Surviving patients)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\nRed: miRNASeqGene; Blue: RNASeq2GeneNorm; Green: RPPA Array")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##               [,1]        [,2]
##   [1,]  4.94001790  0.04297872
##   [2,]  5.28049205  3.54633585
##   [3,] -3.51965070 -2.17536767
##   [4,] -1.73787362 -0.12166340
##   [5,] -1.61049508  0.41007927
##   [6,]  1.20421603  8.19201295
##   [7,]  3.11587810 -7.51119423
##   [8,]  8.56349208  0.73575705
##   [9,]  0.13531717 -0.40140813
##  [10,] -1.61347896  2.76810081
##  [11,] -1.21703648  2.88475750
##  [12,] -0.16348880  2.62351063
##  [13,] -1.79961063  3.63248300
##  [14,]  0.45257270  0.89604357
##  [15,] -0.11877857  2.96074323
##  [16,] -0.88282326 -0.93416485
##  [17,] -4.56601914 -4.36861174
##  [18,] -1.34521242  1.07513705
##  [19,] -0.80907468  0.99553528
##  [20,] -2.04997521 -0.02868829
##  [21,]  0.69704766  0.76944843
##  [22,]  0.23609348  0.83260082
##  [23,]  2.41448394  2.83104672
##  [24,] -2.47282376  1.49227880
##  [25,] -0.57184425 -0.37164780
##  [26,] -3.26326063  0.24182625
##  [27,]  1.02925675 -0.40300228
##  [28,] -1.96280944 -0.60016656
##  [29,]  0.55765130 -4.64331611
##  [30,]  1.39413515 -4.07885516
##  [31,] -1.80430259  0.64937754
##  [32,]  5.68985437 -2.86560998
##  [33,] -0.62948745 -0.93487593
##  [34,] -6.53406637  4.62060264
##  [35,]  0.99150340  0.01308239
##  [36,] -0.53403335  1.76926740
##  [37,]  0.16077241 -2.47147314
##  [38,]  1.50471759  0.50383567
##  [39,]  6.14796426  5.95698029
##  [40,] -0.23722818  1.74014168
##  [41,] -0.10603031  1.29163511
##  [42,] -1.27042630  0.83886740
##  [43,]  1.57970363 -0.39435362
##  [44,] -0.03333145  8.43815653
##  [45,] -0.41503001  0.83367757
##  [46,]  0.81508623  0.01280806
##  [47,]  6.26924432 -0.85203410
##  [48,]  0.02330205  1.42596656
##  [49,] -5.50103840  5.99429414
##  [50,]  1.08231441 -1.93469156
##  [51,] -0.19773931  0.38863089
##  [52,]  0.32830241 -1.48261713
##  [53,] -4.93503350  5.12606887
##  [54,] -0.41908109 -0.06642642
##  [55,]  0.44651929  0.30192108
##  [56,]  0.23770811  1.61567504
##  [57,]  5.86344223 -5.00530388
##  [58,]  4.28239877 -0.33879027
##  [59,] -5.44534102  1.63104711
##  [60,] -6.89741173  1.54872735
##  [61,]  5.82453197  1.69317344
##  [62,]  3.11376642 -3.54189173
##  [63,] -0.82790024  0.32807523
##  [64,] -1.46014663 -0.09533553
##  [65,] -4.04762389  5.24181924
##  [66,] -4.35519250 -2.78491557
##  [67,]  2.25344901  0.46930386
##  [68,]  2.44963182  0.03021716
##  [69,] -0.41662175 -1.64121349
##  [70,] -0.32077345  0.35835872
##  [71,]  2.45794264 -7.75093857
##  [72,] -2.29021824 -7.49336650
##  [73,] -1.13883107  1.31332498
##  [74,]  0.58151277 -0.94692619
##  [75,]  4.13300885 -1.60976984
##  [76,]  3.84665889 -3.06409740
##  [77,]  3.45022238 -3.65271708
##  [78,]  0.54742168  4.19469921
##  [79,]  2.73149317 -0.77243765
##  [80,]  1.13967657  0.66906322
##  [81,]  1.94609739 -3.57068766
##  [82,]  8.07162868  2.40283078
##  [83,] -8.39996434 -1.55956544
##  [84,] -8.62326923 -0.84004584
##  [85,]  0.23335609 -1.64990478
##  [86,]  1.12166185 -0.78671148
##  [87,] -2.10045474 -5.50672460
##  [88,] -3.37104754 -3.73748000
##  [89,]  1.19561181 -1.50000889
##  [90,] -0.43081367  1.18133659
##  [91,] -0.66896070  0.74922935
##  [92,] -0.61631437  8.46991960
##  [93,]  2.23973366 -2.63174113
##  [94,] -0.69016817 -1.16200845
##  [95,]  1.57792983  4.55408633
##  [96,]  5.53445143  2.40990786
##  [97,]  0.72807527 -0.89983607
##  [98,]  0.11471245  0.44797394
##  [99,]  2.58749727 -0.51230820
## [100,]  0.69444273  0.41380159
## [101,]  2.33651669  5.79593489
## [102,]  6.46259673  5.48489027
## [103,]  6.86668424  4.97883846
## [104,]  2.53617171  2.20610088
## [105,]  8.18105571 -1.92387205
## [106,] -3.50150549  1.55757040
## [107,] -0.10506843 -0.25162529
## [108,] -6.30600508  5.00029252
## [109,]  1.81880925 -7.93192860
## [110,] -2.35544094 -1.08076441
## [111,]  3.98825537 -0.81408029
## [112,] -4.32660523 -2.09691055
## [113,] -0.56387288  0.06392528
## [114,]  2.03270795 -0.48853285
## [115,]  0.09634740  0.23805374
## [116,] -1.46369755 -0.50184174
## [117,]  2.04183450 -2.11711604
## [118,] -1.24496120 -0.34829220
## [119,] -1.17974296  0.07353099
## [120,]  0.44935770 -1.49961175
## [121,]  0.28989271 -2.20731259
## [122,] -0.38470700 -0.17148676
## [123,] -1.72228564 -0.78838230
## [124,] -0.56896733  1.10801406
## [125,]  8.28886490  1.17895496
## [126,]  0.88398485  0.83286434
## [127,]  0.02741326 -1.44565632
## [128,] -3.17403350  5.10434311
## [129,] -2.97138961  4.53149454
## [130,]  0.98545127  4.89477426
## [131,] -2.56657377  3.56137291
## [132,] -2.12726494  2.02801809
## [133,] -3.53485339 -4.98321389
## [134,]  0.56340026  8.33454960
## [135,]  4.50233362  7.28146842
## [136,]  1.11769909  0.28437854
## [137,]  1.32611202  1.24555401
## [138,]  0.64912762  3.29518500
## [139,] -4.60578221 -7.27078356
## [140,] -1.14406606 -1.28925358
## [141,] -0.37590705  1.72385608
## [142,] -0.27030232  0.69139733
## [143,] -1.46651114 -0.91036371
## [144,] -1.22532094  8.57295863
## [145,] -6.51939336 -5.79432286
## [146,]  3.31885411  0.22632398
## [147,] -1.36409118 -1.78151028
## [148,]  1.13497887 -0.35206298
## [149,] -0.90764516  0.66492218
## [150,] -0.75457600 -0.99228640
## [151,] -0.96942007 -0.21019031
## [152,] -0.36792693 -0.41523301
## [153,] -1.50846971  0.61168739
## [154,]  0.09521578  0.83913345
## [155,] -8.07453943  4.24263426
## [156,] -0.92697918  0.85856076
## [157,] -5.27834437  3.82000478
## [158,] -0.13878192 -0.39626436
## [159,]  3.24114692 -1.78865543
## [160,] -2.55643217  2.34234316
## [161,] -0.95874813  2.81123257
## [162,] -4.13888485  4.56030178
## [163,] -6.50114923  8.75549094
## [164,] -0.27651879  4.33708050
## [165,] -5.85506639  5.71000672
## [166,] -1.02264728  3.27286531
## [167,] -1.88441011  7.02143968
## [168,] -3.83096899  8.19413824
## [169,] -0.27600734 -0.80571691
## [170,]  2.80832487  0.51116778
## [171,] -0.47957642  6.24498824
## [172,] -0.50740491 -0.77826796
## [173,]  3.86883383 -1.19541545
## [174,] -0.55790140  0.27750028
## [175,]  1.93563394 -2.51296403
## [176,] -0.26707266 -1.46858208
## [177,] -0.48510901 -2.84426835
## [178,] -1.00888466  0.26667193
## [179,] -1.37890534 -0.43660482
## [180,] -4.02252815  6.74766226
## [181,] -3.27004761  6.82127270
## [182,] -1.92210427  3.38224968
## [183,] -4.31146157 -3.64810056
## [184,] -0.14806123  0.10063905
## [185,] -3.32632613  7.42148479
## [186,] -4.28805505  7.93268313
## [187,]  1.48718829 -1.22597105
## [188,] -1.84517927  4.60259422
## [189,]  4.91789548  6.92440081
## [190,] -8.63612854  0.50376122
## [191,]  0.44936466  5.88983216
## [192,] -4.53747069  0.94080661
## [193,]  1.91953305  3.91023877
## [194,] -6.33190920 -2.81084642
## [195,]  0.25404109 -1.35565112
## [196,] -1.54173417 -2.50641940
## [197,] -2.30648375  4.90117110
## [198,] -4.23300124  1.72972071
## [199,] -0.75960206  4.28218623
## [200,] -2.96784171  6.17354698
## [201,]  0.10774232  1.16751585
## [202,]  0.78490245  1.02251033
## [203,] -0.08712960 -0.74298804
## [204,] -1.29646534  7.45575018
## [205,] -1.37658250  4.41658443
## [206,] -7.08647578  8.26416232
## [207,] -2.35889434  6.39549889
## [208,]  0.31263153 -4.15084069
## [209,] -0.19824163 -1.29444426
## [210,]  3.62700275 -4.95251902
## [211,]  0.24634333  0.19267033
## [212,] -5.65742239 -0.88413417
## [213,] -4.22445664 -0.60135166
## [214,] -5.53776836 -1.46333249
## [215,] -5.06810120 -0.84723200
## [216,] -4.93740052 -1.23334391
## [217,] -4.79429829  0.21677753
## [218,] -3.28704527 -1.10600682
## [219,]  7.27428652 -2.80461569
## [220,] -2.13569807 -2.30508213
## [221,] -4.50695763 -1.15256438
## [222,] -4.08711131 -0.96740339
## [223,] -5.73822436 -2.28085134
## [224,] -5.31762766 -2.02559589
## [225,] -4.81751926 -0.37785463
## [226,] -4.87298579 -0.10402143
## [227,] -5.17961550 -0.46280805
## [228,] -3.77461233 -0.99066253
## [229,]  1.03425970  1.60746058
## [230,] -0.72376324  1.88921766
## [231,]  0.40727852  2.45822193
## [232,]  0.55674964  1.24709755
## [233,]  0.05966812  1.96549872
## [234,] -0.63630634  1.26825637
## [235,]  0.55122322  1.93183762
## [236,]  0.75877620  0.90431121
## [237,]  1.01793908  1.91025068
## [238,] -0.06859399  2.10468473
## [239,] -0.37413207  2.48836701
## [240,] -0.34334989  1.43959932
## [241,] -5.80474172  9.25472573
## [242,] -2.05644582  2.65141291
## [243,]  5.42238904  6.67553827
## [244,] -2.51826814  6.00976475
## [245,]  0.80949537  2.41991276
## [246,] -3.11213997 -7.29180453
## [247,] -0.80488518 -0.01245419
## [248,] -0.13280413  1.02080521
## [249,]  1.56128955  0.79811509
## [250,]  0.94294937 -1.13878770
## [251,]  2.16514964 -0.15489518
## [252,]  4.30493737  0.71110481
## [253,]  0.09118648  0.03450715
## [254,]  0.59908169 -0.50625002
## [255,]  1.86090447  1.34388255
## [256,]  0.39830157  1.21012695
## [257,] -0.28596095  0.02011224
## [258,]  0.27135478  2.10045337
## [259,] -1.30775800 -0.07610585
## [260,] -2.93932463 -5.67634188
## [261,] -0.11948071  0.58589646
## [262,] -0.85758368 -0.78408513
## [263,]  8.42072059 -0.76502333
## [264,] -2.70581622  0.42671454
## [265,]  0.35587920 -0.81547261
## [266,]  1.41354366  1.50886683
## [267,]  0.31906905  0.37859560
## [268,]  3.04531820 -1.10742471
## [269,]  3.08051543  1.20769191
## [270,]  8.34341768 -1.29386414
## [271,] -1.60981365  0.24695936
## [272,]  0.77480047 -0.28537892
## [273,]  2.02206351  1.61986018
## [274,] -0.89787833 -2.90734715
## [275,]  3.13156102  0.80142098
## [276,] -6.13495846  5.37333031
## [277,] -1.55878084  3.78915005
## [278,] -1.49257524  2.46952097
## [279,] -1.07528242 -0.04138906
## [280,] -0.99411743  1.51857214
## [281,] -1.51163268  3.53986592
## [282,] -0.09608383  0.82647269
## [283,] -0.08247487 -2.72740435
## [284,] -1.29723913  0.45185367
## [285,]  0.41892053  1.45433801
## [286,] -4.96869276  1.08869117
## [287,] -4.90741995  7.57959970
## [288,] -2.51972388 -0.61486406
## [289,] -0.10653068  1.84623972
## [290,] -1.05837147  1.09932191
## [291,] -1.88663635  2.18039645
## [292,]  0.72934286  0.50567093
## [293,]  0.84581194  0.66739022
## [294,] -1.64304397  0.99414980
## [295,] -3.78670573  6.18023242
## [296,] -0.48320386  0.39347785
## [297,] -1.22354809  1.76092060
## [298,] -1.41164101  1.60215667
## [299,] -0.55939305  0.52442610
## [300,] -3.78539436  1.34845287
## [301,]  0.67947663  1.64132959
## [302,] -0.71458279 -2.48912767
## [303,]  1.92963902  1.13426288
## [304,] -1.77698631 -2.56385105
## [305,]  0.95531281  2.60497253
## [306,]  2.20746863 -1.23860739
## [307,]  1.08047637 -4.44754638
## [308,] -0.51240261 -1.79092245
## [309,]  2.53253330 -5.17702319
## [310,] -1.92736017 -7.88506228
## [311,] -0.73160606 -1.66758602
## [312,]  2.61347657 -1.24684819
## [313,]  1.79798385 -1.47297123
## [314,] -0.34147902 -5.86104463
## [315,]  2.09212264 -4.00090004
## [316,]  0.39607956 -0.95059141
## [317,] -0.65793159  0.94534120
## [318,] -1.34684266 -6.13638116
## [319,]  2.38808073 -1.67376514
## [320,] -2.86742751  0.99970948
## [321,] -0.20126403 -1.65526907
## [322,] -8.62693095 -0.18990246
## [323,] -3.51635414 -4.47943144
## [324,] -2.11070452 -6.18515375
## [325,] -5.27369071 -5.56910050
## [326,]  1.30916735 -0.04738228
## [327,]  0.50511386 -2.47109878
## [328,]  6.58819390 -6.28288908
## [329,] -2.26791748 -0.01763512
## [330,] -7.05123884  3.80685356
## [331,] -1.33519113  0.60702924
## [332,] -4.75940155  3.26501970
## [333,]  5.71452372 -1.76332171
## [334,]  0.36539667 -2.76537368
## [335,]  4.06300209  3.00878705
## [336,] -6.93659183 -4.08686071
## [337,]  4.37681431 -2.34642291
## [338,]  6.06323408 -2.06490606
## [339,]  1.25017723 -6.20533262
## [340,] -1.98715106 -0.28360037
## [341,] -0.92915060 -0.34420610
## [342,]  0.17262707 -0.10377041
## [343,]  1.89228595 -0.99101052
## [344,] -5.31504802 -6.76894559
## [345,] -0.66676227 -1.38417752
## [346,]  2.77512404 -3.63416063
## [347,]  0.17213714  0.55705149
## [348,]  3.28797390  3.88850687
## [349,] -0.82091770 -0.49884174
## [350,]  3.80848401  3.55461239
## [351,]  0.11249387 -1.12563008
## [352,]  4.44254832  2.63234479
## [353,] -3.34313725  0.82853471
## [354,]  0.04995932 -8.27418283
## [355,]  8.30692066  1.79241293
## [356,] -1.96189188 -3.75530473
## [357,] -1.03221153  0.65012581
## [358,]  4.65980403  1.54762187
## [359,]  5.70857759  6.25242729
## [360,]  5.00958196  1.24591396
## [361,]  1.24095403 -8.13782218
## [362,] -0.65432670 -5.71657577
## [363,]  5.21401247 -5.65342207
## [364,]  4.85351268 -3.42237063
## [365,] -2.74246131 -0.22772156
## [366,] -9.15238249 -2.40601127
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## [368,] -1.04305105 -0.59405380
## [369,] -4.16575589  2.36939189
## [370,] -0.41326391 -1.29993185
## [371,]  1.89121700  1.85874187
## [372,]  2.81867534  1.82789097
## [373,] -1.08814475 -2.36989215
## [374,]  0.73140143 -0.12375324
## [375,]  6.37945483 -4.12263823
## [376,]  1.77057578  4.97184514
## [377,]  8.33889809  0.26441991
## [378,]  1.33742542  0.85389439
## [379,]  1.93103149 -5.94686133
## [380,]  0.91151688  0.40753080
## [381,]  5.68366998  2.97228757
## [382,]  6.32621535 -0.15067669
## [383,] -1.15904634 -0.68119177
## [384,]  2.45940407  3.70335323
## [385,]  0.10994413 -0.86097057
## [386,] -1.42097306 -3.10010789
## [387,]  3.37584371 -0.14842197
## [388,]  0.94740185 -1.54373185
## [389,]  2.48648434  0.59908806
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## [391,]  1.42647162  3.62152731
## [392,]  0.73170784 -1.53951843
## [393,]  0.71810484  0.14429291
## [394,]  1.66525947 -2.89896537
## [395,] -2.52204414  0.53784134
## [396,]  8.50620791 -0.23468769
## [397,] -8.32958418  3.51410353
## [398,]  2.03583377  2.95123959
## [399,] -5.91683333 -6.37831563
## [400,]  5.63404209  0.89041777
## [401,]  1.01758551 -3.31207691
## [402,]  0.63706223 -8.08768730
## [403,] -1.19506838 -7.77920572
## [404,] -1.97407510  8.44145755
## [405,] -8.51538438  2.83506962
## [406,] -0.81052326  5.33082668
## [407,]  0.32116765 -0.55058256
## [408,]  1.55809735  0.17446822
## [409,]  0.03031948 -1.26377022
## [410,] -2.10701616 -0.71704602
## [411,] -0.70474181 -0.35182321
## [412,] -0.39496002 -0.55749558
## [413,] -1.41792793 -1.20177284
## [414,] -1.10568281 -0.82243796
## [415,] -0.33176875 -0.62776935
## [416,] -3.32461152 -0.69250276
## [417,]  0.60204576 -0.60418383
## [418,] -1.85489149  1.23284382
## [419,] -0.50259812 -0.91818754
## [420,] -2.18227265 -1.95063868
## [421,] -1.85800429 -1.21013335
## [422,]  0.31419112 -1.11452238
## [423,] -1.72190415  0.07812903
## [424,]  0.40464931 -0.07060950
## [425,] -2.24638968 -1.37415097
## [426,] -5.35895991  2.69989446
## [427,] -2.04475332 -1.59297505
## [428,] -0.93218540 -1.38588017
## [429,]  0.89265534 -0.52746005
## [430,] -1.50710398 -1.29554741
## [431,] -2.65708242 -2.50568376
## [432,]  1.06136654  1.36993544
## [433,]  0.41425221 -0.23583900
## [434,]  0.86000880 -0.13163961
## [435,] -1.60098065  1.35092834
## [436,] -3.09494351  1.87958263
## [437,]  1.38390952 -0.92186625
## [438,] -0.66824013  1.51341078
## [439,]  0.89569195 -2.25966452
## [440,]  0.49940784 -0.34614412
Ugraph(PcorP.d.min, type = "fancy", lay = "layout_with_fr", Vsize = 2, Vcex = .3, prune = T, cut = 0.5,
       Vcolor = Colors.d.min,
       main = "All Numerical data (Deceased patients)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\nRed: miRNASeqGene; Blue: RNASeq2GeneNorm; Green: RPPA Array")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##                [,1]        [,2]
##   [1,] 12.457545222  4.48911522
##   [2,]  0.967424171 -3.81004179
##   [3,] -4.172137359  7.22383321
##   [4,] 11.146837993  8.61841445
##   [5,] 10.153494171 -0.79899775
##   [6,]  6.922735910  0.62576691
##   [7,] -4.404564430 -1.22013057
##   [8,]  2.962396319  1.53936699
##   [9,]  1.936738573  1.53422602
##  [10,]  6.355139494 -2.87851793
##  [11,]  0.194135080 -5.47368687
##  [12,]  2.782697875  5.20976191
##  [13,]  3.048027843 -0.83839343
##  [14,]  3.172721070 -1.43824282
##  [15,]  3.548336951  0.37026242
##  [16,]  3.275461659 -0.68996709
##  [17,]  2.861795167  2.72681117
##  [18,]  3.383179866  5.31201113
##  [19,]  5.415350234  3.90642868
##  [20,]  6.621918498  2.67817041
##  [21,]  5.580140903  0.92519738
##  [22,]  3.382595800  3.58455231
##  [23,]  3.605175771  2.26244303
##  [24,]  3.738135219  2.42743172
##  [25,]  3.942098746  2.80907894
##  [26,]  4.084606056  4.91005580
##  [27,]  5.353459754 -0.06212577
##  [28,]  2.087659139  4.08633650
##  [29,]  2.311054436  0.92374477
##  [30,] -5.629906671  3.55532692
##  [31,]  4.137043050  2.38878737
##  [32,]  2.734066800  2.84887653
##  [33,]  9.398789672  1.93492859
##  [34,]  6.985216665  7.99188196
##  [35,] -5.784225849  1.50888101
##  [36,]  3.184689231  2.42892982
##  [37,]  4.655662132 -2.97219464
##  [38,]  1.685628730  3.03922829
##  [39,]  1.962466661  3.20857925
##  [40,]  1.082253235  3.50645599
##  [41,] -0.521125374  3.63968676
##  [42,]  4.135846764  5.26958931
##  [43,]  4.973080769  4.37579355
##  [44,]  1.783603767  3.54124134
##  [45,]  4.274723754  4.18504415
##  [46,]  4.264752506  3.84795107
##  [47,] -3.726371371  7.43914391
##  [48,]  3.111628809  1.70125074
##  [49,]  3.566920753  4.04613981
##  [50,] -3.628026339  7.91076572
##  [51,]  2.862291186  1.37534920
##  [52,]  2.156186180 -2.26325554
##  [53,]  4.416689018  2.43576427
##  [54,]  3.354041047  2.11179937
##  [55,]  4.005353866  3.00805985
##  [56,]  4.650985623 -1.55062638
##  [57,]  3.434542793  1.79982566
##  [58,]  3.785569779  1.21990063
##  [59,]  5.769654670  1.58000414
##  [60,]  3.172025453  0.51344415
##  [61,]  6.362491738  3.22194576
##  [62,]  2.723078086  6.04471761
##  [63,]  5.915877950  6.38540278
##  [64,]  5.135986956  9.69524306
##  [65,] -0.978873567  1.49406426
##  [66,]  2.843604576  6.54564358
##  [67,] -4.523014620  0.14257983
##  [68,]  4.514340231  3.77914474
##  [69,]  5.265363907  2.32601221
##  [70,]  4.379635109 -3.99323262
##  [71,] 12.282744474  6.65276374
##  [72,]  6.260940299  3.97925095
##  [73,]  7.183388731  1.92442015
##  [74,]  0.582855751 -0.59214937
##  [75,]  4.757875533 -0.20044411
##  [76,]  2.991520585  3.98084237
##  [77,]  1.531203678  2.27861104
##  [78,] -0.789032593 -4.89527223
##  [79,]  6.655338880  3.62029847
##  [80,]  3.908965941  1.90630109
##  [81,]  1.733935162  1.12591636
##  [82,] -2.795722915 -0.06560876
##  [83,] 11.896171924  6.78410445
##  [84,]  5.570790135  3.50897025
##  [85,]  0.816441842  4.67346073
##  [86,] -5.821374441  2.98952324
##  [87,]  7.453146982  2.42536970
##  [88,]  9.496687860  1.15368884
##  [89,]  9.399429548  0.61098774
##  [90,]  3.257020885  3.37603464
##  [91,]  1.870486294  2.57562279
##  [92,]  2.541336122  5.18543600
##  [93,]  4.999262125  2.57790188
##  [94,]  5.346073149  7.82359908
##  [95,]  4.561351107  1.37773759
##  [96,]  2.841315058  1.58387771
##  [97,] -1.802987997  7.39602860
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##  [99,] -0.576584193  4.39495720
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## [101,]  3.778920268  4.77770815
## [102,]  5.197154083  5.37765965
## [103,]  1.141046019  7.49678013
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## [105,]  2.639632533  1.44416325
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## [108,]  6.680139133  6.18779560
## [109,] -0.140096659  1.43285224
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## [111,]  3.588579766  7.84896118
## [112,]  6.906441629  3.87043978
## [113,]  9.689393896  2.72529698
## [114,]  1.515713190  0.86512299
## [115,]  0.768692311  0.13578476
## [116,] -0.353411847 -5.22950048
## [117,]  2.732398735  4.33486201
## [118,]  1.871910402  3.43767292
## [119,]  1.552106139 -1.80122018
## [120,]  9.895069978  6.54992986
## [121,]  0.642216836  7.34308752
## [122,]  3.109106517  9.59275315
## [123,]  6.414536225  2.33943314
## [124,]  6.071763360 10.40567563
## [125,] -1.837287599 -4.30468406
## [126,] -5.398413671  4.38817267
## [127,]  2.672554323 11.85316529
## [128,]  4.263412052 -0.02246351
## [129,]  2.418261807  2.94561549
## [130,] -2.429065360  5.37373594
## [131,]  1.661059101  4.14813697
## [132,]  3.583077661  2.87446038
## [133,]  3.488565209  2.08957457
## [134,]  3.408939586  0.69054525
## [135,]  3.743601913  2.71003937
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## [137,]  2.625246197  2.47417897
## [138,]  2.724559035  3.15616429
## [139,]  2.281741949  1.30432710
## [140,]  4.198249589  2.25858553
## [141,] 12.374664456  5.23880586
## [142,]  6.566254246 -1.71119537
## [143,]  4.779085752  1.03975802
## [144,]  2.389918899  2.80319106
## [145,]  3.204002231 -2.25359764
## [146,]  3.849345442 -1.55353781
## [147,] -4.457817109 -0.76294165
## [148,]  3.122960418 -1.01822170
## [149,]  4.384372156 -0.75306596
## [150,]  5.476337040 -1.43813146
## [151,] -0.220332377 11.58079917
## [152,] -0.775549233  5.85466057
## [153,] 10.640959636  8.84268863
## [154,] -0.822215922 10.84568505
## [155,] 10.517231793  8.32401928
## [156,] -3.201594804  8.26380042
## [157,] -5.136803333  0.29382461
## [158,]  3.238533703  3.01125404
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## [160,]  5.873372327  1.78522000
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## [163,]  3.246442539  4.97129488
## [164,]  0.553429370  2.05462391
## [165,]  3.274360029  1.43222373
## [166,]  3.209491328  4.70513951
## [167,] -2.574015515 -0.62990230
## [168,] -0.256148142  6.83868034
## [169,]  0.828807984  0.85285503
## [170,]  9.492140322  9.54491981
## [171,] 11.790323832  6.01052252
## [172,]  1.833961210 -0.11070570
## [173,]  1.696524921  2.19438630
## [174,]  1.092842510  2.49298984
## [175,]  2.762921958  2.38311150
## [176,] -2.209615678  1.73689715
## [177,]  7.667533429  6.81784490
## [178,]  0.575828402  1.43107980
## [179,]  2.815317324  3.76605011
## [180,]  0.947981074  1.57300491
## [181,]  1.872466317 -2.69949051
## [182,]  3.166429105  1.98439599
## [183,]  3.431151706 -3.63396344
## [184,]  3.000039575  2.13932062
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## [186,]  3.117833538 -2.94472629
## [187,]  2.380285521 -1.47554831
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## [189,]  2.720274267 -2.69446585
## [190,]  4.064974650 -3.91284935
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## [192,]  1.790908009 -2.36099280
## [193,]  3.944382504 -0.42583259
## [194,]  4.273893818 -1.92195634
## [195,]  3.134074948 -3.55852717
## [196,]  1.946403732  2.92973536
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## [198,]  6.056167905  4.99087749
## [199,] -1.201796886 -2.05267791
## [200,]  0.276706488  6.48624386
## [201,]  3.287267665  2.40006708
## [202,]  3.921259669  3.11059921
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## [204,]  3.148470951  2.78074110
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## [228,]  4.694217449  2.30299696
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## [230,]  1.094239502 -0.98503636
## [231,]  2.303652876 -3.18288865
## [232,]  1.761075935 -0.38308361
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## [234,]  2.435174594  1.71574333
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## [236,]  6.550604926 -2.16297321
## [237,]  2.276555355  3.25825709
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## [254,]  1.614602771  4.85671495
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## [256,]  4.476932294  1.94610290
## [257,]  4.058715798  1.10980615
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## [260,]  3.265575469  1.16233340
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## [268,]  3.252910263 -0.49800153
## [269,]  4.148953357 -2.45789401
## [270,]  1.488000444  3.15029248
## [271,]  2.931170047  2.56657177
## [272,]  3.724855041  1.58079124
## [273,]  2.173292515  2.88399283
## [274,]  3.235047608  4.23129058
## [275,] -4.374924630  6.69795145
## [276,] -0.657147498  4.01592518
## [277,]  3.068529239  4.36735041
## [278,] -0.069670936  3.06719559
## [279,] -0.232500808 11.13474171
## [280,]  5.889676884  2.62483283
## [281,] -4.927335155 -0.45495542
## [282,]  3.294557075  1.82411701
## [283,]  3.557321739  2.43874421
## [284,]  1.098916615  4.82248691
## [285,]  2.190485469  5.47179372
## [286,]  0.597418585  5.20903581
## [287,]  1.531129202  2.52514138
## [288,]  4.825549809  2.80936501
## [289,] 12.393610820  5.98913632
## [290,]  2.594148433  1.88137761
## [291,]  0.714584488  1.09775938
## [292,] -0.337796345  0.52832525
## [293,]  1.689902286 12.01876102
## [294,]  2.991834263  3.55453944
## [295,] -1.037644621  3.87929486
## [296,] -4.895838486  0.74005807
## [297,] -0.169509784  1.99343554
## [298,]  5.153811731  1.73514858
## [299,]  1.350252613  7.15532417
## [300,]  0.389677253 11.40650890
## [301,] -5.613753284  2.59666890
## [302,] -0.954529845  0.02640668
## [303,]  4.725774694 -3.53656316
## [304,]  3.665214428 -4.01300578
## [305,]  3.059318411 -0.60013371
## [306,]  0.373692621  2.66654619
## [307,]  3.416808467  1.38735871
## [308,]  1.246681713  2.23682279
## [309,]  3.415439502 -1.09862193
## [310,]  1.638453458  1.80303534
## [311,]  1.731331711  1.45497218
## [312,] -2.974638168  8.65787759
## [313,]  4.685704352  2.11093284
## [314,]  3.384067554  4.12536527
## [315,]  3.460766335  7.56321487
## [316,]  2.872309396 -4.21321667
## [317,]  1.904442279  0.47976484
## [318,]  5.185403533  2.91158702
## [319,]  0.904292322 -5.70144490
## [320,]  3.589982178  2.75878003
## [321,]  3.230941534 -1.60428335
## [322,]  4.040818145  4.21874752
## [323,]  5.315799998  1.05913155
## [324,] -4.473939690  6.09271218
## [325,]  4.158580056  4.57315240
## [326,]  9.562202925 -2.79447655
## [327,]  2.534959424 -3.56293602
## [328,]  5.782846838  5.15408721
## [329,]  3.067868410  5.33927040
## [330,]  3.593221389  5.27875750
## [331,]  0.550547352  3.68929964
## [332,] -1.416045262  9.90654355
## [333,]  3.597094935  4.95965605
## [334,]  3.516275901  4.53106034
## [335,]  2.012546746  4.26537668
## [336,]  0.880316146  0.52380214
## [337,]  6.604572954  8.20930028
## [338,]  1.254011723 -1.31865736
## [339,]  3.697931608  8.78309170
## [340,]  4.668291796  8.32522337
## [341,]  9.176817599 -2.59561107
## [342,]  5.269620010  3.13363518
## [343,]  7.982783583 -0.31360958
## [344,]  1.978848938 -5.60466983
## [345,] -2.046774736  2.92041282
## [346,]  8.822866011  4.23329054
## [347,]  0.309732971  0.38772055
## [348,]  1.459975746 11.54795707
## [349,] -0.450895408  3.09062783
## [350,]  0.900472987  3.93966430
## [351,] 10.891636378  3.64886044
## [352,]  1.817122675  1.75217001
## [353,]  6.815703403 -5.49291541
## [354,]  5.384883112  4.31964050
## [355,] -5.620710646  2.10377456
## [356,]  6.361914105  0.59812880
## [357,]  5.782041942  5.93602698
## [358,] -2.019207057  6.55075224
## [359,]  5.015447545  3.57576425
## [360,]  2.348621812  2.52679410
## [361,] -1.168384137  2.97218028
## [362,] -1.340419484  5.56686911
## [363,] -2.105019831  9.33248953
## [364,]  0.541868539 11.86126392
## [365,]  2.691762879  1.78840359
## [366,] -1.308565140  7.92242269
## [367,]  2.400219088  2.18207289
## [368,] 10.054828516  8.95306264
## [369,]  3.862133982  4.22664432
## [370,]  0.160048803 -3.01419989
## [371,]  7.979831473  2.39906442
## [372,] 11.467470710  8.06875792
## [373,]  4.898501692  4.90705750
## [374,] -1.709210446 -1.50512682
## [375,]  4.684029271  6.80403731
## [376,]  2.160915623  1.42846139
## [377,]  0.612064024  5.88923483
## [378,]  2.807343859  1.96540211
## [379,] -0.520908676 -0.30221348
## [380,]  2.529502631  1.16545771
## [381,]  3.456465629  2.45697070
## [382,]  2.659945182  3.04481723
## [383,]  4.968370231  3.28470581
## [384,]  2.207112265 -0.11558823
## [385,]  9.135056902 10.12712809
## [386,]  8.819006658 10.50750555
## [387,]  2.396661984  7.55413373
## [388,] 10.527508792  9.33470995
## [389,]  3.094446495  1.59126311
## [390,] -5.438396550  4.95758960
## [391,] -1.094205048  4.62595264
## [392,]  2.415136693  3.86116086
## [393,]  6.185329607  3.40483730
## [394,]  4.320529051  1.20559341
## [395,]  1.101693671 -0.31489595
## [396,]  2.597729089  6.91520856
## [397,]  2.012737004  1.85258380
## [398,] 11.735269989  7.58994804
## [399,]  1.370644764  2.04756270
## [400,]  4.905860582  3.84141139
## [401,]  2.431185229  8.95672484
## [402,]  4.054731818  8.13231111
## [403,]  5.732199979  9.52481359
## [404,] 11.510522970  7.17468504
## [405,]  5.640653612  2.36701971
## [406,]  2.348295604  0.44424460
## [407,]  3.969948666  5.52217250
## [408,] 10.040550721  9.52206266
## [409,]  0.127933500  4.92229621
## [410,]  0.275128414  1.01807517
## [411,]  7.497075764 -1.46621533
## [412,]  6.257279690  4.60212336
## [413,]  8.227624628  1.25656912
## [414,]  9.668344980  9.97727246
## [415,] -3.120033698  2.99527017
## [416,]  2.986645409  3.34343354
## [417,] 12.371613277  3.93844162
## [418,]  0.406843396  1.81819183
## [419,]  6.291574180  0.02509002
## [420,] -2.930343511  4.51359878
## [421,]  6.295934159  8.37963855
## [422,]  5.136035983 -2.05912689
## [423,]  2.186037209  2.68556107
## [424,]  5.984766742  0.90389069
## [425,] -5.766573831  4.00693406
## [426,] -3.496292935 -2.53427522
## [427,] -0.190670536  1.06390585
## [428,]  2.242120856  2.08655798
## [429,]  8.279302128  4.16102730
## [430,] -3.839525004 -2.02795527
## [431,]  0.003471395  5.44747087
## [432,] 10.953658760  8.01420126
## [433,]  6.290613556  9.27657381
## [434,]  5.269315561  0.37775696
## [435,] 11.440846174  6.56971258
## [436,] -5.408638473  1.46235132
## [437,]  3.151357431 11.98352873
## [438,]  0.682903711  8.72488425
## [439,]  4.591095074  0.45655874
## [440,]  3.825738467  3.49766456
## [441,]  0.154240854 -0.07493186
## [442,]  1.312446097  3.48100310
## [443,]  3.770957206  9.55647721
## [444,]  7.557470323  5.53488714
## [445,] -0.478016034 -1.55904877
## [446,]  4.293790862  2.79880873
## [447,]  1.159264512  4.04944852
## [448,] -2.506641088  8.92286181
## [449,]  2.432920092  1.98128074
## [450,]  1.926980883  2.35277355
## [451,]  1.484382524  1.33823042
## [452,]  5.354267314  6.92447337
## [453,]  3.430786993  3.42663045
## [454,]  1.030329927 11.74495008
## [455,]  0.481141069  4.50924221
## [456,] 12.023406375  5.56292852
## [457,] 12.140530482  4.82232819
## [458,]  6.103171029  2.93546361
## [459,] -0.530798550  4.95830831
## [460,]  2.146051349 11.82039942
## [461,]  8.714610733  6.75769737
## [462,] -4.221146638 -1.66955543
## [463,] -0.024702124  2.35388165
## [464,] -4.994585319  3.01690199
## [465,] -3.106406200  3.99122703
## [466,]  4.695113362  4.49034629
## [467,]  4.449164610  7.07743077
## [468,]  3.526841000  2.00257253
## [469,]  3.504708222  0.90708156
## [470,]  4.127056546  1.66404595
## [471,]  3.801838951  3.30767547
## [472,]  3.900502598  1.68826062
## [473,]  2.678279583  3.59124823
## [474,]  2.501957433  4.81788249
## [475,]  1.933416939  2.20056949
## [476,]  3.729512890  3.64246442
## [477,]  3.881395674  7.02355022
## [478,]  2.227362780  2.24243547
## [479,]  2.506321792  3.26072976
## [480,]  5.331595322  1.50021628
## [481,] -0.236070405  4.24079380
## [482,]  6.121365011  1.92680490
## [483,]  2.628527902  4.70808412
## [484,]  3.880501984  2.10360872
## [485,]  5.165120323  1.37472166
## [486,] -0.129562320  2.59776771
## [487,] -1.074610456 11.17639745
## [488,]  3.566587080  3.10862719
## [489,]  2.633124826  3.93986701
## [490,]  4.994039819  5.71289413
## [491,]  4.982189065  1.94208767
## [492,]  4.386278344  4.41618098
## [493,]  2.651710380  1.58970219
## [494,]  3.012851406  3.23738906
## [495,]  2.266831510  3.46629030
## [496,]  2.678579458  3.43777936
## [497,]  0.962589902  3.02036640
## [498,]  1.957168862  2.73130697
## [499,]  1.558713613  2.77179745
## [500,]  2.427941098  3.73994960
## [501,]  1.333212537  1.76037404
GGM.all.alive.min = as.data.frame(GGMnetworkStats(P0.all.alive.min$sparseParCor, as.table = T))
GGM.all.dead.min = as.data.frame(GGMnetworkStats(P0.all.dead.min$sparseParCor, as.table = T))
GGM.all.alive.min.order = GGM.all.alive.min[order(GGM.all.alive.min$degree, decreasing = T), ]
GGM.all.dead.min.order = GGM.all.dead.min[order(GGM.all.dead.min$degree, decreasing = T), ]

#Output top 5%
GGM.all.alive.min.order[1:round(nrow(GGM.all.alive.min.order) * 0.05), ]
##                degree betweenness   closeness eigenCentrality nNeg nPos
## G6PD.RPPA         168   9845.7516 0.001371742       0.9453448   88   80
## PDCD4.RPPA        164   7375.9317 0.001377410       1.0000000   82   82
## TFRC.RPPA         163   8708.7640 0.001394700       0.9947696   81   82
## IGFBP2.RPPA       156   7023.3638 0.001360544       0.9777160   78   78
## GATA3.RPPA        151   6648.2105 0.001319261       0.8748329   82   69
## GAPDH.RPPA        145   6958.8498 0.001319261       0.8802702   81   64
## FASN.RPPA         144   6722.5402 0.001291990       0.8293319   75   69
## MYH11.RPPA        135   3912.3948 0.001312336       0.8981209   68   67
## SQSTM1.R2Gn       127   2625.0369 0.001305483       0.9124490   69   58
## GAPDH.R2Gn        123   2680.9953 0.001261034       0.8271108   74   49
## EEF2.R2Gn         121   2671.5811 0.001283697       0.8668991   60   61
## ATM.RPPA          117   3268.5137 0.001302083       0.7922948   65   52
## HSPA1A.R2Gn       101   1444.3206 0.001233046       0.7088702   51   50
## SYP.R2Gn           96   1069.2180 0.001201923       0.7043772   49   47
## TGM2.R2Gn          94   1489.8359 0.001215067       0.6604615   48   46
## FN1.R2Gn           90   1434.0092 0.001172333       0.6484073   46   44
## TTF1.RPPA          86   1207.6249 0.001190476       0.6322846   52   34
## hsa-mir-206        81    910.2568 0.001164144       0.5812520   41   40
## hsa-mir-216b       81    654.0348 0.001206273       0.6334391   49   32
## MSH6.RPPA          81   1181.7849 0.001177856       0.6161348   46   35
## RPS6.R2Gn          80    918.8240 0.001199041       0.5985855   46   34
## SERPINE1.R2Gn      79    879.6468 0.001183432       0.5969885   38   41
## hsa-mir-383        78   1178.5310 0.001176471       0.5915320   39   39
## IGFBP2.R2Gn        78    822.4111 0.001183432       0.6074979   42   36
## hsa-mir-137        75    396.7228 0.001173709       0.6337584   38   37
## hsa-mir-122        74    775.8305 0.001141553       0.5259819   43   31
## PTEN.RPPA          74    952.6622 0.001199041       0.5805945   34   40
## hsa-mir-1251       72    626.9116 0.001180638       0.5992614   37   35
## hsa-mir-1305       70    899.4704 0.001157407       0.5188689   34   36
## hsa-mir-135a-2     69    405.9010 0.001204819       0.5766997   34   35
## hsa-mir-429        68    659.2839 0.001156069       0.4851670   33   35
## RBM15.RPPA         68    409.9047 0.001187648       0.5692950   35   33
## EGFR.RPPA          68    587.1310 0.001152074       0.5176734   38   30
## hsa-mir-873        67    568.0790 0.001177856       0.5188944   34   33
## hsa-mir-1258       66    727.7608 0.001153403       0.4568887   28   38
##                mutualInfo variance partialVar
## G6PD.RPPA      0.20204728 1.223906          1
## PDCD4.RPPA     0.18646575 1.204983          1
## TFRC.RPPA      0.17529089 1.191593          1
## IGFBP2.RPPA    0.17936641 1.196459          1
## GATA3.RPPA     0.15863750 1.171913          1
## GAPDH.RPPA     0.20634456 1.229177          1
## FASN.RPPA      0.12961406 1.138389          1
## MYH11.RPPA     0.16381684 1.177999          1
## SQSTM1.R2Gn    0.10818455 1.114253          1
## GAPDH.R2Gn     0.14126705 1.151732          1
## EEF2.R2Gn      0.10511574 1.110839          1
## ATM.RPPA       0.12044601 1.128000          1
## HSPA1A.R2Gn    0.07793508 1.081052          1
## SYP.R2Gn       0.05796163 1.059674          1
## TGM2.R2Gn      0.07862171 1.081795          1
## FN1.R2Gn       0.05434665 1.055851          1
## TTF1.RPPA      0.06133206 1.063252          1
## hsa-mir-206    0.05072024 1.052029          1
## hsa-mir-216b   0.05203778 1.053416          1
## MSH6.RPPA      0.04204393 1.042940          1
## RPS6.R2Gn      0.04975728 1.051016          1
## SERPINE1.R2Gn  0.05419564 1.055691          1
## hsa-mir-383    0.05170538 1.053065          1
## IGFBP2.R2Gn    0.07318399 1.075928          1
## hsa-mir-137    0.04902682 1.050249          1
## hsa-mir-122    0.04732383 1.048461          1
## PTEN.RPPA      0.05635560 1.057974          1
## hsa-mir-1251   0.04476313 1.045780          1
## hsa-mir-1305   0.03970958 1.040509          1
## hsa-mir-135a-2 0.04117753 1.042037          1
## hsa-mir-429    0.04198681 1.042881          1
## RBM15.RPPA     0.04688632 1.048003          1
## EGFR.RPPA      0.04771507 1.048872          1
## hsa-mir-873    0.04364237 1.044609          1
## hsa-mir-1258   0.03230406 1.032832          1
GGM.all.dead.min.order[1:round(nrow(GGM.all.dead.min.order) * 0.05), ]
##                degree betweenness    closeness eigenCentrality nNeg nPos
## FN1.R2Gn          241  21986.3754 0.0012953368       1.0000000  122  119
## MYH11.RPPA        211  12701.5501 0.0012562814       0.9686400  109  102
## TFRC.RPPA         211  12453.4951 0.0012610340       0.9951659  113   98
## GATA3.RPPA        206  13737.5974 0.0012562814       0.9363932  106  100
## HSPA1A.R2Gn       192   9672.2517 0.0011668611       0.9061097   88  104
## GAPDH.RPPA        173   8678.2950 0.0011933174       0.8415024   90   83
## GAPDH.R2Gn        166   5633.4569 0.0011876485       0.8468065   83   83
## EEF2.R2Gn         155   4693.5947 0.0011820331       0.8341235   75   80
## SYP.R2Gn          140   3063.8040 0.0011467890       0.7893836   65   75
## FASN.RPPA         139   2868.0309 0.0011415525       0.7532201   67   72
## RPS6.R2Gn         137   3012.6079 0.0011325028       0.7464105   80   57
## SQSTM1.R2Gn       136   3326.3740 0.0011520737       0.7578852   74   62
## ATM.RPPA          128   3596.3706 0.0010905125       0.7191972   66   62
## PDCD4.RPPA        125   2441.9224 0.0011415525       0.7494179   68   57
## CTNNB1.R2Gn       122   3012.5137 0.0011223345       0.6899288   57   65
## G6PD.RPPA         120   3714.3493 0.0011185682       0.6842527   63   57
## hsa-mir-122       111   1452.1374 0.0010834236       0.6695921   59   52
## hsa-mir-499       109   2079.9301 0.0010482180       0.5844634   58   51
## hsa-mir-124-3      95    813.6369 0.0010893246       0.6365824   53   42
## ADAR.R2Gn          93   1423.4171 0.0010582011       0.5500443   53   40
## CCND1.R2Gn         91   1067.6224 0.0010928962       0.5488467   47   44
## TUBA1B.R2Gn        88    900.5169 0.0010537408       0.5687985   40   48
## MSH6.RPPA          87    793.7687 0.0010649627       0.5188161   41   46
## hsa-mir-135a-2     83    419.6551 0.0010718114       0.6034760   39   44
## hsa-mir-34b        83    510.9229 0.0010718114       0.5668682   44   39
## hsa-mir-3662       83    448.5196 0.0010741139       0.5837708   44   39
## hsa-mir-137        81    873.6690 0.0010764263       0.5300668   43   38
## hsa-mir-1197       77   2031.1748 0.0010235415       0.3370275   20   57
## hsa-mir-200a       76    486.1722 0.0010010010       0.4742470   33   43
## hsa-mir-216a       75    538.8298 0.0010471204       0.4880645   37   38
## PTEN.RPPA          75    442.6918 0.0010288066       0.4798766   43   32
## hsa-mir-656        74   1495.5721 0.0009930487       0.3396354   18   56
## hsa-mir-206        72    375.6631 0.0010090817       0.4888304   31   41
## hsa-mir-124-2      71    318.6859 0.0010172940       0.4987746   39   32
## hsa-mir-329-1      70   1363.3646 0.0009643202       0.2790516   20   50
##                mutualInfo variance partialVar
## FN1.R2Gn       0.20482810 1.227314          1
## MYH11.RPPA     0.15896348 1.172295          1
## TFRC.RPPA      0.16071776 1.174353          1
## GATA3.RPPA     0.14906958 1.160754          1
## HSPA1A.R2Gn    0.14647187 1.157742          1
## GAPDH.RPPA     0.12226536 1.130054          1
## GAPDH.R2Gn     0.10105121 1.106333          1
## EEF2.R2Gn      0.07879298 1.081980          1
## SYP.R2Gn       0.06862457 1.071034          1
## FASN.RPPA      0.06840337 1.070797          1
## RPS6.R2Gn      0.06674720 1.069025          1
## SQSTM1.R2Gn    0.06562988 1.067831          1
## ATM.RPPA       0.05239122 1.053788          1
## PDCD4.RPPA     0.06294247 1.064966          1
## CTNNB1.R2Gn    0.05641572 1.058037          1
## G6PD.RPPA      0.04567924 1.046739          1
## hsa-mir-122    0.03210511 1.032626          1
## hsa-mir-499    0.04446979 1.045473          1
## hsa-mir-124-3  0.03124435 1.031738          1
## ADAR.R2Gn      0.03969341 1.040492          1
## CCND1.R2Gn     0.04207702 1.042975          1
## TUBA1B.R2Gn    0.02590811 1.026247          1
## MSH6.RPPA      0.04582815 1.046894          1
## hsa-mir-135a-2 0.03239691 1.032927          1
## hsa-mir-34b    0.03310570 1.033660          1
## hsa-mir-3662   0.02983565 1.030285          1
## hsa-mir-137    0.02385379 1.024141          1
## hsa-mir-1197   0.01909698 1.019280          1
## hsa-mir-200a   0.02661153 1.026969          1
## hsa-mir-216a   0.02448512 1.024787          1
## PTEN.RPPA      0.02010053 1.020304          1
## hsa-mir-656    0.01957779 1.019771          1
## hsa-mir-206    0.02767371 1.028060          1
## hsa-mir-124-2  0.02568195 1.026015          1
## hsa-mir-329-1  0.01695770 1.017102          1
Colors.min.plot.a <- rownames(GGM.all.alive.min.order)
Colors.min.plot.a[grep("hsa", rownames(GGM.all.alive.min.order))] <- "miRNASeqGene"
Colors.min.plot.a[grep(".RPPA", rownames(GGM.all.alive.min.order))] <- "RPPA Array"
Colors.min.plot.a[grep(".R2Gn", rownames(GGM.all.alive.min.order))] <- "RNASeq2GeneNorm"
ggplot(GGM.all.alive.min.order, aes(x = reorder(rownames(GGM.all.alive.min.order), -degree), y = degree, color = Colors.min.plot.a)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.all.alive.min.order$degree), linetype = "dashed", color = "red")  +
  # 36th unit: top 5%
  geom_hline(yintercept = GGM.all.alive.min.order[36,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "All Data", guide = guide_axis(angle = 90)) +
  scale_color_manual(values = c("red", "blue", "green3"))+
  ggtitle("Variables sorted by degree, FDR = 1-1e-14, Surviving Patients")

Colors.min.plot.d <- rownames(GGM.all.dead.min.order)
Colors.min.plot.d[grep("hsa", rownames(GGM.all.dead.min.order))] <- "miRNASeqGene"
Colors.min.plot.d[grep(".RPPA", rownames(GGM.all.dead.min.order))] <- "RPPA Array"
Colors.min.plot.d[grep(".R2Gn", rownames(GGM.all.dead.min.order))] <- "RNASeq2GeneNorm"
ggplot(GGM.all.dead.min.order, aes(x = reorder(rownames(GGM.all.dead.min.order), -degree), y = degree, color = Colors.min.plot.d)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.all.dead.min.order$degree), linetype = "dashed", color = "red")  +
  # 36th unit: top 5%
  geom_hline(yintercept = GGM.all.dead.min.order[36,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "All Data", guide = guide_axis(angle = 90)) +
  scale_color_manual(values = c("red", "blue", "green3"))+
  ggtitle("Variables sorted by degree, FDR = 1-1e-14, Deceased Patients")

FDRcut 1-1e-6:

P0.all.alive.6 = sparsify(opt.all.alive$optPrec, threshold = "localFDR", FDRcut=1-1e-6)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  11475 
## - Corresponding to 4.68 % of possible edges 
## 
P0.all.dead.6 = sparsify(opt.all.dead$optPrec, threshold = "localFDR", FDRcut=1-1e-6)
## Step 1... determine cutoff point
## Step 2... estimate parameters of null distribution and eta0
## Step 3... compute p-values and estimate empirical PDF/CDF
## Step 4... compute q-values and local fdr
## Step 5... prepare for plotting

## 
## - Retained elements:  12207 
## - Corresponding to 4.98 % of possible edges 
## 
PcorP.a.6 = pruneMatrix(P0.all.alive.6$sparseParCor)
Colors.a.6 <- rownames(PcorP.a.6)
Colors.a.6[grep("hsa", rownames(PcorP.a.6))] <- "red"
Colors.a.6[grep(".RPPA", rownames(PcorP.a.6))] <- "green"
Colors.a.6[grep(".R2Gn", rownames(PcorP.a.6))] <- "cyan"
PcorP.d.6 = pruneMatrix(P0.all.dead.6$sparseParCor)
Colors.d.6 <- rownames(PcorP.d.6)
Colors.d.6[grep("hsa", rownames(PcorP.d.6))] <- "red"
Colors.d.6[grep(".RPPA", rownames(PcorP.d.6))] <- "green"
Colors.d.6[grep(".R2Gn", rownames(PcorP.d.6))] <- "cyan"
set.seed(42)
Ugraph(PcorP.a.6, type = "fancy", lay = "layout_with_fr", Vsize = 2, Vcex = .3, prune = T, cut = 0.5,
       Vcolor = Colors.a.6,
       main = "All Numerical data (Surviving patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\nRed: miRNASeqGene; Blue: RNASeq2GeneNorm; Green: RPPA Array")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##               [,1]          [,2]
##   [1,] -4.30701986  -3.742865820
##   [2,] -5.45488551  -0.929373442
##   [3,]  0.21956118   1.016141793
##   [4,]  8.17462438  -5.686197543
##   [5,] -0.98981579 -10.714395830
##   [6,]  0.09135922  -1.831589281
##   [7,]  0.72862731  -1.609869599
##   [8,] -5.26430024   1.295265842
##   [9,]  8.45781515  -2.589686377
##  [10,] -5.49392788  -5.163060979
##  [11,] -0.82581588  -1.624163064
##  [12,]  2.09544760  -1.123058660
##  [13,]  1.68872994  -0.956949181
##  [14,]  1.71236375  -1.160444408
##  [15,]  1.44079185  -1.041208938
##  [16,] -1.14828922  -1.016236574
##  [17,] -0.40661424   0.555186883
##  [18,] -0.11010096  -6.827509097
##  [19,] -0.51171889  -0.972942746
##  [20,]  1.03213654   3.097717050
##  [21,]  0.62080581  -1.208090794
##  [22,] -0.41205562  -2.196580119
##  [23,]  0.34062271  -2.791589425
##  [24,] -0.92427913  -1.554878825
##  [25,] -0.36775346  -0.776370655
##  [26,] -1.15632820  -4.061059443
##  [27,] -0.97065128   0.279545973
##  [28,]  0.29883787  -1.654061858
##  [29,]  1.69110892  -2.224453058
##  [30,] -0.92524829  -0.432603981
##  [31,] -0.48721594  -3.152805140
##  [32,] -3.53330010  -1.475145261
##  [33,] -2.55657753  -3.732936441
##  [34,] -0.03960073  -2.944089140
##  [35,] -1.32180494  -5.124458877
##  [36,] -1.05033392  -1.389517744
##  [37,]  3.62804607   0.212395902
##  [38,] -0.15113867  -2.272225843
##  [39,]  0.57890268  -1.027566424
##  [40,] -0.65802981   0.398272342
##  [41,] -2.13151509  -1.932582501
##  [42,] -5.61820484   3.671229302
##  [43,] -5.09666905   4.018986562
##  [44,] -3.41719262   0.102571243
##  [45,]  0.14566207  -1.114695437
##  [46,]  0.28028731  -2.137551636
##  [47,] -0.25032360   3.123318054
##  [48,]  0.28066219  -2.087291491
##  [49,] -1.95443624  -2.180663595
##  [50,]  4.04546589   2.722665280
##  [51,] -5.66181906  -8.402399337
##  [52,] -0.49748388  -0.670898381
##  [53,] -1.06154584  -1.860130644
##  [54,]  3.88831445   2.522947335
##  [55,] -8.88591149   3.168537301
##  [56,] -4.29119545   1.747312977
##  [57,]  0.38529391  -1.218603703
##  [58,]  3.96062630  -1.622934796
##  [59,] -0.39606179  -0.067672784
##  [60,] -0.28115381  -2.040262524
##  [61,] -0.29548609  -2.768153986
##  [62,]  3.26910970  -0.373302231
##  [63,] -0.77493485  -1.256442929
##  [64,] -0.63329795  -2.555137174
##  [65,]  0.22137136  -0.816815897
##  [66,]  1.78974895  -2.765335794
##  [67,] -2.47659755  -0.579621613
##  [68,]  8.37689314  -0.523715600
##  [69,] -7.22548161  -7.646980920
##  [70,] -3.27710171  -3.418737284
##  [71,]  1.67675052   2.256851117
##  [72,]  1.97126620   3.228781334
##  [73,] -4.05626329   0.371650436
##  [74,]  2.16963523  -3.090962208
##  [75,]  0.01464990  -1.816125547
##  [76,] -0.22648397  -0.680362611
##  [77,]  2.99565495  -0.013104907
##  [78,]  4.31530751  -3.296256041
##  [79,]  2.50730821  -7.950600234
##  [80,] -2.12673294   6.331936289
##  [81,] -5.27675080  -6.620493711
##  [82,]  8.56370870  -2.101774600
##  [83,] -6.75265622  -7.368966780
##  [84,]  2.80944071   2.240127775
##  [85,]  2.35426641  -4.135211076
##  [86,] -2.15055002  -2.638851834
##  [87,] -2.22795840  -2.442558121
##  [88,]  0.11218789  -2.628540317
##  [89,] -9.13054655   0.365478219
##  [90,]  0.09874020  -1.928476064
##  [91,]  7.97189821  -4.085372338
##  [92,]  4.11201486  -6.638364201
##  [93,] -3.19770708   4.249397202
##  [94,] -0.05840919  -0.937891324
##  [95,] -0.93432150  -2.359823134
##  [96,] -3.20745171   0.475471236
##  [97,]  5.81381155  -8.602111184
##  [98,] -4.22798707  -0.390806606
##  [99,] -4.36747631   0.113815718
## [100,] -2.05908173  -4.069590918
## [101,] -0.49144989  -4.018254903
## [102,] -0.85535514  -2.610822047
## [103,] -1.29564679  -3.511335443
## [104,] -5.81829892  -2.167231691
## [105,] -5.97624863   0.868487013
## [106,] -4.95817041   1.895048443
## [107,] -6.49785977   1.178505166
## [108,] -1.67074613  -1.243424889
## [109,] -1.44637353  -0.581280832
## [110,] -4.81077101  -1.819893192
## [111,] -1.31477862  -4.288406271
## [112,] -1.84429984  -1.064402957
## [113,]  0.57371944  -1.357189472
## [114,]  0.18558792  -1.782372187
## [115,] -0.55756909  -7.991547204
## [116,]  7.23432786  -4.963876585
## [117,] -2.60891187  -3.165697999
## [118,] -0.77869989  -2.711575373
## [119,] -3.96140924   2.053680530
## [120,] -3.61321750  -1.858258496
## [121,] -1.11901011  -1.329271757
## [122,] -1.52012483  -1.173587557
## [123,] -0.32508375   0.203015004
## [124,]  0.09389619  -2.307012719
## [125,]  1.51034315   0.391991858
## [126,]  8.36845809  -3.476478549
## [127,] -2.86682514   5.155096126
## [128,] -2.14059395   4.101842844
## [129,]  8.27611843   0.029091600
## [130,] -2.50364777  -2.931033470
## [131,] -9.72142632  -1.335642575
## [132,]  8.37781618  -3.059381762
## [133,] -2.37218970   7.835796270
## [134,] -6.38047828   0.389917360
## [135,] -9.12720120  -4.732590306
## [136,]  1.06040125  -3.217489703
## [137,] -9.70304526  -2.020325162
## [138,] -6.86135992   2.126705507
## [139,] -1.60904084  -1.941546506
## [140,]  3.95582759  -0.573934901
## [141,]  6.99706030  -7.542521678
## [142,] -8.52922561   3.589166178
## [143,]  4.63211903  -6.137363326
## [144,] -0.57765076  -3.096003494
## [145,] -3.01459581  -2.593416123
## [146,] -5.47790317  -7.791551947
## [147,]  2.38836386  -2.710324933
## [148,] -9.59852550  -3.805515143
## [149,] -0.56258348  -0.756234392
## [150,]  7.73091742  -5.039057978
## [151,] -2.11765294  -1.265572000
## [152,] -0.10820500  -1.687197897
## [153,]  0.06669448  -3.051593602
## [154,] -2.24270644  -1.048732717
## [155,]  0.13221739  -0.944199950
## [156,] -0.44969902  -1.986725055
## [157,] -0.95524185  -2.896559567
## [158,] -0.59050817  -0.032897985
## [159,] -0.23082323  -1.195473708
## [160,]  0.45046469  -2.015432529
## [161,] -0.02577953  -2.109204116
## [162,]  1.93736801  -3.562076303
## [163,]  8.09890055  -4.700875363
## [164,] -0.21265373  -0.367932534
## [165,] -0.45472058  -1.165489965
## [166,]  3.42025377  -0.689281860
## [167,]  3.62543497  -0.694860180
## [168,] -0.57211364   1.798897755
## [169,]  3.30457653   1.921446324
## [170,]  1.57237776  -1.127825347
## [171,]  1.44600134  -0.521985748
## [172,] -3.10762982   3.657648887
## [173,]  7.37078590   1.480438960
## [174,] -4.03443475  -2.280974945
## [175,]  6.27519003  -1.605473846
## [176,]  2.88002489  -3.855109440
## [177,]  6.61732881  -7.676357888
## [178,] -0.05424570   2.264793852
## [179,] -5.14907369   0.254240183
## [180,] -1.18559856  -0.800051677
## [181,]  2.56817794   1.623035935
## [182,] -0.91124947  -0.745894777
## [183,] -0.39359880  -3.791971730
## [184,] -7.00710086   5.468238178
## [185,]  2.94342959  -7.591975249
## [186,] -1.38825779  -1.558087440
## [187,] -8.83994146  -5.289992824
## [188,]  0.40229478  -1.495599424
## [189,] -6.22239222  -7.999661002
## [190,] -0.67557646  -1.763757833
## [191,]  0.81381360   2.603791830
## [192,] -1.06122903  -2.401632011
## [193,]  2.78027659   3.582011700
## [194,] -3.88848656  -7.603864600
## [195,] -1.69033497   0.046209556
## [196,]  7.60623764  -6.270526835
## [197,]  6.29722070  -8.164908010
## [198,]  0.63730082  -0.211352759
## [199,] -0.74843692  -1.372471640
## [200,] -1.98798597 -10.613789433
## [201,]  0.55690069  -2.214657819
## [202,] -1.13437709  -2.035247852
## [203,] -0.78221836  -2.912053986
## [204,] -7.62799729  -0.627300811
## [205,] -0.92678506  -0.927951368
## [206,]  0.43459643  -1.615359534
## [207,] -0.45196262  -1.628378275
## [208,]  4.19337740  -1.336762014
## [209,] -1.08278448  -0.690478920
## [210,]  3.82738901  -0.980998829
## [211,] -0.29206930  -1.367878346
## [212,] -7.35107389   5.033677545
## [213,] -3.40892562  -2.879482982
## [214,]  1.94897018  -0.656465383
## [215,]  1.89843531  -0.982373434
## [216,]  3.67538265  -1.395308820
## [217,]  4.24034066  -0.734277337
## [218,]  1.85243574   0.412690771
## [219,] -1.49267583 -10.710941611
## [220,]  4.13534152  -1.048256281
## [221,]  1.66507039  -0.748959208
## [222,]  3.72690063  -1.186383573
## [223,]  3.43025669  -0.236025640
## [224,] -0.86467087  -1.876102313
## [225,] -0.90201785  -3.947041613
## [226,]  4.78375557  -2.288747969
## [227,]  0.17842148  -1.531988440
## [228,] -1.83048114   0.379850134
## [229,] -1.29238167  -1.783534271
## [230,] -2.19347963   0.027555747
## [231,] -1.72586549  -2.251349758
## [232,] -1.33585893  -3.265443863
## [233,] -0.10842585  -1.272522484
## [234,]  0.21836652  -1.085887785
## [235,]  3.56432318  -0.404487473
## [236,]  3.79962455  -0.199536160
## [237,]  2.29200603  -1.188012984
## [238,]  2.51730826  -1.985304199
## [239,]  7.15008503  -6.799533781
## [240,] -6.07322609  -0.958881288
## [241,] -0.74287575  -1.954754212
## [242,]  3.64641057  -0.907558992
## [243,]  4.19506849   0.244462134
## [244,] -0.03159250  -0.314825932
## [245,]  2.78075333  -0.446309984
## [246,] -5.00416534  -1.529478865
## [247,] -5.24436196  -3.944953505
## [248,] -5.38115013  -3.479532557
## [249,] -1.87104290  -4.506167652
## [250,] -3.35589909  -2.226727928
## [251,]  1.51563932   1.163040677
## [252,] -1.47196248  -1.606249545
## [253,] -0.84285607  -3.593397429
## [254,]  2.98206348  -0.831169141
## [255,] -3.00035778  -3.761977461
## [256,]  2.42656792  -0.153428576
## [257,]  3.49305788  -0.018267575
## [258,] -0.21716593  -1.690954200
## [259,] -1.28837409  -1.473892183
## [260,] -0.64680947  -1.232870033
## [261,] -6.99224228  -3.421033802
## [262,]  3.58451758  -1.879719780
## [263,]  2.19399882   0.045794321
## [264,]  3.92048893   0.010026343
## [265,]  2.89424693   0.361636867
## [266,] -3.39022417  -3.953198926
## [267,] -0.29706482  -0.985858734
## [268,] -0.83570658  -5.603233049
## [269,] -0.81354624  -1.063143044
## [270,]  1.16226904  -4.775909346
## [271,] -0.03662345  -4.144519918
## [272,]  0.41540166  -5.005807015
## [273,]  0.88272024  -5.257997896
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## [520,]  1.71053812  -8.067229505
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## [524,] -0.83711713  -1.997631923
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## [526,] -2.12897954  -1.689074932
## [527,]  0.92569773  -2.184735813
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## [530,] -0.35416407  -1.261096660
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## [533,] -0.96912688  -1.061546907
## [534,]  1.35771878  -3.227722841
## [535,] -0.73996936  -0.590518197
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## [538,] -1.28878925  -3.815038828
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## [544,]  2.19557709  -4.870995144
## [545,]  0.74905952  -0.815478823
## [546,]  0.25844289  -2.431570627
## [547,] -1.14719896  -2.193756279
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## [549,]  0.52939553  -3.875465370
## [550,] -2.07717508  -0.150446280
## [551,] -0.65325765  -1.627871117
## [552,] -1.45396964  -1.049545318
## [553,] -1.71430036  -1.539962932
## [554,] -2.52992713  -2.661803504
## [555,] -0.47777668  -0.445656370
## [556,]  0.45024748  -1.871819365
## [557,] -0.13792869  -0.429674313
## [558,] -0.07532972  -1.852101081
Ugraph(PcorP.d.6, type = "fancy", lay = "layout_with_fr", Vsize = 2, Vcex = .3, prune = T, cut = 0.5,
       Vcolor = Colors.d.6,
       main = "All Numerical data (Deceased patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\nRed: miRNASeqGene; Blue: RNASeq2GeneNorm; Green: RPPA Array")
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE
## Warning in type.convert.default(X[[i]], ...): 'as.is' should be specified by
## the caller; using TRUE

##                [,1]          [,2]
##   [1,] -14.33395441   0.913753619
##   [2,] -14.79592384  -1.599094605
##   [3,]  -7.49101184  -8.725372376
##   [4,]  -4.43158485   1.849541553
##   [5,]  -3.72952543  -5.063420453
##   [6,] -14.45070193  -3.586550854
##   [7,]  -8.68726465  -8.053713212
##   [8,]  -8.03690361  -8.305015418
##   [9,]  -6.88957533 -10.609569078
##  [10,]  -2.01840559  -7.741499322
##  [11,]  -9.53014957   0.185366700
##  [12,]  -6.60384036   0.868792221
##  [13,] -10.24968291   3.834584761
##  [14,] -11.07539984   2.733915950
##  [15,] -12.07078435  -3.847555380
##  [16,]  -6.81942254  -2.244497829
##  [17,]  -7.19913028  -1.739295085
##  [18,]  -5.28085489  -5.801814855
##  [19,] -12.46339993  -4.205998401
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##  [21,] -12.52743629   4.192457215
##  [22,]  -4.28883270  -1.186829452
##  [23,]  -6.92502368  -3.691141018
##  [24,]  -7.44323959  -4.016638068
##  [25,]  -7.07768909  -3.809822107
##  [26,]  -6.92710114  -3.400759141
##  [27,]  -5.51864809  -2.060459748
##  [28,]  -4.39265261  -2.327791564
##  [29,]  -6.82916572  -1.034911285
##  [30,]  -4.77049226   0.201766203
##  [31,]  -8.25403816  -1.818644677
##  [32,]  -5.55514083  -1.488178545
##  [33,]  -6.93870709  -2.160933512
##  [34,]  -6.28114387  -1.807762962
##  [35,]  -5.52679248  -2.248684624
##  [36,]  -6.68104985  -2.389169673
##  [37,]  -7.46008694  -0.668802869
##  [38,]  -5.58835235  -0.325145966
##  [39,]  -7.11370525  -0.975134429
##  [40,] -10.11607741  -3.757106747
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##  [48,]  -8.71014515  -4.428029631
##  [49,]  -5.91704171  -0.673103423
##  [50,]  -6.52812999  -1.542302206
##  [51,]  -6.44825121  -0.197762337
##  [52,]  -0.10686491  -3.553835703
##  [53,] -11.02157251   1.830559324
##  [54,]   1.85047745  -1.189390085
##  [55,]   0.38025088  -1.351015012
##  [56,]  -3.37072062  -3.531420202
##  [57,]  -4.31602977  -0.300265697
##  [58,]  -4.26391930   0.230942487
##  [59,]  -5.33240649  -0.539427948
##  [60,]  -4.92845516  -0.775478789
##  [61,]  -4.50218858  -3.139952568
##  [62,]  -3.76282450   2.033808287
##  [63,]  -7.04817396  -1.510530532
##  [64,]  -4.68371611  -1.581504391
##  [65,]  -8.33153595   7.008190290
##  [66,]  -3.41728456   4.957187206
##  [67,] -11.28252103  -0.970635090
##  [68,]  -6.54100671  -1.272473245
##  [69,]  -7.42855842  -5.603284437
##  [70,]  -6.12652208  -1.248391544
##  [71,]  -5.76862611  -1.660332502
##  [72,]  -4.92121839  -2.332961339
##  [73,]  -9.00341904  -4.431466604
##  [74,]  -5.89112874  -2.877360407
##  [75,]  -7.05230642  -2.995007278
##  [76,]  -7.89701834  -1.673367037
##  [77,]  -7.73378356  -1.527035569
##  [78,]   4.13348612  -0.994469821
##  [79,]  -4.40126502  -6.697291023
##  [80,]  -4.77214297  -4.113756520
##  [81,] -14.47941892   0.381104560
##  [82,]  -3.43187404  -2.526502016
##  [83,]  -2.06759314  -0.856196615
##  [84,]   1.79833873  -0.443716383
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## [456,] -14.50134021  -2.845161529
## [457,]  -4.11985942  -3.506578796
## [458,]  -2.88890204  -4.792178944
## [459,]  -2.22002137  -0.405378424
## [460,]  -6.75399955  -1.830665272
## [461,]  -2.99605640  -1.188012300
## [462,]  -5.64269116  -1.157699905
## [463,]  -4.21399868  -5.109406352
## [464,]  -2.27672413  -2.216488337
## [465,]  -5.32751239  -2.721672700
## [466,]  -6.15632538  -2.055398613
## [467,]  -0.50771489  -4.147351314
## [468,]  -6.35964354  -1.544584021
## [469,]  -4.20308331  -2.969979930
## [470,] -13.33915859   2.761641681
## [471,]  -8.05660754  -0.552721977
## [472,]  -1.45747114   2.301023933
## [473,]   0.30059619  -5.863064708
## [474,]  -3.24381342  -0.464416191
## [475,]  -5.59593711   7.139725874
## [476,]  -3.40719032  -7.849218643
## [477,]  -6.74718119  -1.726136343
## [478,]  -2.99205528 -10.043035082
## [479,]  -3.69285057  -1.507921049
## [480,]  -5.35817421  -2.588609491
## [481,]  -1.70948713   2.660869823
## [482,]  -8.42606635  -2.288139364
## [483,]  -6.06843116  -0.702781449
## [484,]  -7.50764023  -0.322174936
## [485,]  -7.23846818  -0.431728234
## [486,]  -6.20100987  -0.766209114
## [487,]   0.43565397  -2.334150777
## [488,]  -6.93127063  -0.841707597
## [489,]  -7.11761042  -0.880938825
## [490,] -10.94724272  -8.800344697
## [491,]  -5.34205083  -5.354537894
## [492,]  -3.09961588  -3.068902148
## [493,]  -2.63084108  -0.036495554
## [494,]  -0.15683620  -4.729768044
## [495,]   1.22414591  -0.263231957
## [496,]  -5.96456808 -10.693997041
## [497,]  -4.65966567  -3.431666925
## [498,]  -7.42217837  -2.125535407
## [499,]   3.38316175  -4.999403687
## [500,]  -1.72168423  -5.940917629
## [501,]  -3.65812483  -1.997954712
## [502,]  -2.14720478  -6.276310416
## [503,]  -3.89242403  -3.812278962
## [504,]  -8.36442263  -0.682463447
## [505,]  -5.11507020  -6.262680136
## [506,]   3.89757616  -1.408697866
## [507,] -11.97340140   4.733260500
## [508,] -12.02588691  -7.715852943
## [509,]  -3.06863982  -5.458936945
## [510,]  -3.89698996  -3.268025258
## [511,]   4.11505343  -3.715627338
## [512,]  -4.50464866   0.984763042
## [513,]  -3.16511083   1.024751078
## [514,]  -2.32628002   1.003150308
## [515,]  -5.66432496  -0.738047453
## [516,]   3.83260139  -3.395509344
## [517,]  -7.07754986  -0.205574254
## [518,]  -4.32165333  -3.878159894
## [519,]   0.06874606   2.141018777
## [520,]  -2.53946269  -0.327800877
## [521,]  -8.74098985  -0.678520256
## [522,]  -4.90291101  -1.370442755
## [523,]  -7.83094165   0.013577853
## [524,]  -1.71140299  -2.361115761
## [525,]  -6.09461319   4.263957331
## [526,]  -7.63523619   0.243468931
## [527,]  -6.52348791  -2.046223001
## [528,]  -9.16619483   0.067058238
## [529,]  -1.17057076  -1.269185745
## [530,]  -6.91759237   1.448329723
## [531,]  -2.88351644  -6.618586324
## [532,]  -2.58513981   1.498316277
## [533,]  -8.26577358  -2.659359416
## [534,]  -1.96002915  -1.407240670
## [535,]   0.67888448  -4.195865867
## [536,]  -2.82532288   1.992273823
## [537,]  -2.72769884  -4.337732844
## [538,]  -4.10567110  -2.713497273
## [539,]  -4.93212530  -1.509708345
## [540,]  -8.94530528  -2.237298836
## [541,]  -1.11053515  -2.940303320
## [542,]  -5.13150022  -2.571409917
## [543,]  -2.51462476  -2.027585455
## [544,]  -8.27143441   1.488077727
## [545,]  -4.13249885   1.986547569
## [546,]  -4.69657730  -2.096680508
## [547,]  -7.57200412  -1.663253319
## [548,]  -7.31080633   7.357177175
## [549,]  -3.05449924  -6.293101307
## [550,]   0.58296004   4.389097329
## [551,]   4.32016528  -1.753106191
## [552,] -13.92654626   1.970300187
## [553,]  -6.14292827  -2.806153481
## [554,]  -6.19754951  -0.140599559
## [555,]  -7.32970396  -2.516298391
## [556,]  -9.62481209  -9.709668637
## [557,]  -5.52590327   2.173694543
## [558,]  -6.49396900  -0.993923148
## [559,]  -4.03453689  -6.125297242
## [560,]  -5.66191863   0.366700003
## [561,]  -1.31218012   0.801344741
## [562,]   3.94018356  -0.629793892
## [563,]  -3.11900500  -2.812039277
## [564,] -10.77915551  -4.109598289
## [565,]  -7.02057021   0.543062134
## [566,]  -0.83808033   0.431100493
## [567,]  -4.23502750  -4.277048691
## [568,]   2.08037402   3.484804728
## [569,]  -9.57189755   2.671659544
## [570,]  -7.74317246  -0.642726204
## [571,]  -7.77736606   2.111674301
## [572,]  -3.39886792   1.572861582
## [573,]  -4.82536290  -0.227986300
## [574,]  -5.39689382   0.137073723
## [575,]  -6.69064635  -2.048676733
## [576,]  -6.28706421  -3.066062199
## [577,]  -6.76240610  -2.521322543
## [578,]  -5.35862698  -2.312823540
## [579,]  -5.85290831  -2.692832098
## [580,]  -6.00241669  -0.994031892
## [581,]  -4.09635261  -1.264170995
## [582,]  -5.62952268  -1.032235568
## [583,]  -5.19960339  -1.606554971
## [584,]  -2.62438370  -1.636357597
## [585,]  -5.58434448  -1.746415549
## [586,]  -4.93280507  -0.933709489
## [587,]  -7.66350377  -2.309392149
## [588,]  -5.99311430   0.559167341
## [589,]  -5.17527039  -4.405893191
## [590,]  -5.76078997  -0.975898307
## [591,]  -6.52975206  -1.384358370
## [592,]  -6.54788188  -2.728409671
## [593,]  -4.33662236  -0.095719190
## [594,]  -3.56523100  -5.992853861
## [595,]  -5.18429454  -1.246342757
## [596,]  -6.16260500  -1.310295041
## [597,]  -3.81121728  -0.331137069
## [598,]  -7.15754198  -2.273259599
## [599,]  -5.81268634  -3.598745421
## [600,]  -5.36248191  -2.850667028
## [601,]  -5.64272974  -1.983990812
## [602,]  -6.70969766  -1.317196967
## [603,]  -5.35290286  -2.030124104
## [604,]  -4.67106168  -2.950006729
## [605,]  -5.78257815  -0.907473391
## [606,]  -5.72258670  -2.717913222
## [607,]  -4.65955099  -1.805624090
## [608,]  -6.02858797  -2.637572044
GGM.all.alive.6 = as.data.frame(GGMnetworkStats(P0.all.alive.6$sparseParCor, as.table = T))
GGM.all.dead.6 = as.data.frame(GGMnetworkStats(P0.all.dead.6$sparseParCor, as.table = T))
GGM.all.alive.6.order = GGM.all.alive.6[order(GGM.all.alive.6$degree, decreasing = T), ]
GGM.all.dead.6.order = GGM.all.dead.6[order(GGM.all.dead.6$degree, decreasing = T), ]

#Output top 5%
GGM.all.alive.6.order[1:round(nrow(GGM.all.alive.6.order) * 0.05), ]
##               degree betweenness    closeness eigenCentrality nNeg nPos
## G6PD.RPPA        242  13947.5265 0.0011148272       0.9404069  130  112
## TFRC.RPPA        240   9488.6135 0.0011441648       1.0000000  113  127
## IGFBP2.RPPA      230  11586.2395 0.0011223345       0.9603509  124  106
## GATA3.RPPA       226  11771.5770 0.0010905125       0.8960354  117  109
## FASN.RPPA        218   9946.7011 0.0010752688       0.8813835  109  109
## PDCD4.RPPA       218   7204.6302 0.0011001100       0.9343471  109  109
## GAPDH.R2Gn       203  11345.2466 0.0010834236       0.8788641  109   94
## GAPDH.RPPA       202  11130.1275 0.0010775862       0.8068668  110   92
## MYH11.RPPA       196   5378.6923 0.0010672359       0.9013832   97   99
## EEF2.R2Gn        183   2813.6316 0.0010615711       0.8938545   94   89
## SQSTM1.R2Gn      178   3713.0405 0.0010626993       0.8611513   93   85
## ATM.RPPA         169   3209.7988 0.0010482180       0.8095259   87   82
## HSPA1A.R2Gn      168   3813.9873 0.0010256410       0.8012883   83   85
## TGM2.R2Gn        160   3676.8869 0.0010288066       0.7633667   78   82
## FN1.R2Gn         145   2699.6851 0.0009737098       0.6898722   70   75
## TTF1.RPPA        143   1785.4269 0.0009775171       0.7095083   72   71
## SYP.R2Gn         141   1084.7697 0.0009784736       0.7240031   67   74
## hsa-mir-122      135   1562.1181 0.0009910803       0.6866148   69   66
## IGFBP2.R2Gn      133   1373.5930 0.0009980040       0.6665976   73   60
## hsa-mir-137      132   1199.8894 0.0009950249       0.7181450   69   63
## hsa-mir-216b     132   1994.1774 0.0009784736       0.6567626   76   56
## hsa-mir-383      132   1365.0313 0.0009803922       0.6914947   74   58
## SERPINE1.R2Gn    130   1681.4925 0.0009765625       0.6496111   67   63
## MSH6.RPPA        128   1432.8787 0.0009680542       0.6591527   67   61
## RPS6.R2Gn        127    966.9534 0.0009990010       0.6650723   68   59
## hsa-mir-1305     122    736.4864 0.0009643202       0.6287396   60   62
## hsa-mir-296      122   1650.8241 0.0009569378       0.5623403   61   61
## PTEN.RPPA        122   1148.1097 0.0009842520       0.6647700   61   61
## hsa-mir-105-1    121   1097.9026 0.0009891197       0.6137457   58   63
## hsa-mir-206      121    645.5031 0.0009606148       0.6463505   63   58
## hsa-mir-873      119   1513.3086 0.0009871668       0.6161376   59   60
## EGFR.RPPA        118   1461.5681 0.0009920635       0.6433110   62   56
## hsa-mir-429      117    837.9607 0.0009756098       0.6033483   55   62
## hsa-mir-577      117    851.7913 0.0009832842       0.6252330   68   49
## hsa-mir-105-2    115   1060.3877 0.0009680542       0.5942184   61   54
##               mutualInfo variance partialVar
## G6PD.RPPA     0.20232498 1.224246          1
## TFRC.RPPA     0.17774847 1.194525          1
## IGFBP2.RPPA   0.18418301 1.202236          1
## GATA3.RPPA    0.16180693 1.175633          1
## FASN.RPPA     0.13278951 1.142010          1
## PDCD4.RPPA    0.18740383 1.206114          1
## GAPDH.R2Gn    0.14473177 1.155730          1
## GAPDH.RPPA    0.20780696 1.230976          1
## MYH11.RPPA    0.16504122 1.179442          1
## EEF2.R2Gn     0.10732477 1.113296          1
## SQSTM1.R2Gn   0.11023139 1.116536          1
## ATM.RPPA      0.12303163 1.130920          1
## HSPA1A.R2Gn   0.08278371 1.086307          1
## TGM2.R2Gn     0.08313356 1.086687          1
## FN1.R2Gn      0.06071698 1.062598          1
## TTF1.RPPA     0.06633578 1.068585          1
## SYP.R2Gn      0.06053300 1.062403          1
## hsa-mir-122   0.05550907 1.057079          1
## IGFBP2.R2Gn   0.07692004 1.079956          1
## hsa-mir-137   0.05437659 1.055882          1
## hsa-mir-216b  0.05661494 1.058248          1
## hsa-mir-383   0.05681204 1.058457          1
## SERPINE1.R2Gn 0.05936896 1.061167          1
## MSH6.RPPA     0.04732089 1.048458          1
## RPS6.R2Gn     0.05362341 1.055087          1
## hsa-mir-1305  0.04591693 1.046987          1
## hsa-mir-296   0.04221873 1.043123          1
## PTEN.RPPA     0.06021776 1.062068          1
## hsa-mir-105-1 0.04328644 1.044237          1
## hsa-mir-206   0.05293718 1.054363          1
## hsa-mir-873   0.04839765 1.049588          1
## EGFR.RPPA     0.05215606 1.053540          1
## hsa-mir-429   0.04726128 1.048396          1
## hsa-mir-577   0.04077900 1.041622          1
## hsa-mir-105-2 0.04751180 1.048659          1
GGM.all.dead.6.order[1:round(nrow(GGM.all.dead.6.order) * 0.05), ]
##                degree betweenness    closeness eigenCentrality nNeg nPos
## FN1.R2Gn          311  22620.6721 0.0010917031       0.9997554  163  148
## TFRC.RPPA         303  16742.8315 0.0010976948       0.9904256  158  145
## MYH11.RPPA        299  15950.9398 0.0010893246       1.0000000  159  140
## GATA3.RPPA        286  16191.4659 0.0010775862       0.9517245  142  144
## HSPA1A.R2Gn       267  13878.9095 0.0010319917       0.9268846  137  130
## GAPDH.RPPA        264  14260.8928 0.0010482180       0.8735397  138  126
## GAPDH.R2Gn        236   7071.4516 0.0010141988       0.8521681  118  118
## EEF2.R2Gn         234   6513.1973 0.0010193680       0.8830306  107  127
## SYP.R2Gn          209   5317.4972 0.0009861933       0.8328442  100  109
## G6PD.RPPA         203   4259.5733 0.0009746589       0.7947036  110   93
## FASN.RPPA         203   4718.7695 0.0009756098       0.7745888   99  104
## SQSTM1.R2Gn       202   6861.2948 0.0009881423       0.7709728  106   96
## RPS6.R2Gn         193   4282.2118 0.0009746589       0.7720015  101   92
## ATM.RPPA          192   3299.9567 0.0009442871       0.7600591   99   93
## CTNNB1.R2Gn       185   4151.2341 0.0009699321       0.7475606   87   98
## PDCD4.RPPA        182   2796.2805 0.0009689922       0.7805713   92   90
## hsa-mir-499       169   4440.7788 0.0009216590       0.6513608   86   83
## hsa-mir-122       159   1150.3890 0.0009469697       0.7176233   84   75
## TUBA1B.R2Gn       158   2146.6335 0.0009208103       0.6783887   79   79
## ADAR.R2Gn         156   1289.9590 0.0009132420       0.6710976   82   74
## MSH6.RPPA         148   1076.4023 0.0009233610       0.6446882   71   77
## CCND1.R2Gn        146   1986.9998 0.0009293680       0.6198953   74   72
## hsa-mir-137       142   1401.8295 0.0009216590       0.6172048   79   63
## hsa-mir-135a-2    141   1169.9459 0.0009285051       0.6487865   71   70
## hsa-mir-124-3     139    845.2028 0.0009242144       0.6550325   78   61
## hsa-mir-34b       139    948.4423 0.0009149131       0.6313927   67   72
## hsa-mir-3662      139   1004.1023 0.0009174312       0.6439292   73   66
## hsa-mir-124-2     134   1196.4753 0.0009115770       0.6272339   72   62
## RBM15.RPPA        133   1065.1950 0.0008849558       0.5427402   68   65
## hsa-mir-200a      129    838.5248 0.0008756567       0.5605142   68   61
## hsa-mir-216a      129    784.9285 0.0009009009       0.5849457   67   62
## PEA15.RPPA        126    631.1033 0.0009124088       0.5857375   69   57
## PTEN.RPPA         124    757.7862 0.0008880995       0.5712753   70   54
## hsa-mir-1269      123   1025.5123 0.0009074410       0.6004190   55   68
## hsa-mir-3166      123    724.7278 0.0008779631       0.5609606   56   67
##                mutualInfo variance partialVar
## FN1.R2Gn       0.19503360 1.215352          1
## TFRC.RPPA      0.15880524 1.172110          1
## MYH11.RPPA     0.15679643 1.169757          1
## GATA3.RPPA     0.14802433 1.159541          1
## HSPA1A.R2Gn    0.14206675 1.152654          1
## GAPDH.RPPA     0.12355493 1.131512          1
## GAPDH.R2Gn     0.10163009 1.106974          1
## EEF2.R2Gn      0.08044111 1.083765          1
## SYP.R2Gn       0.06894869 1.071381          1
## G6PD.RPPA      0.04879533 1.050005          1
## FASN.RPPA      0.06862232 1.071032          1
## SQSTM1.R2Gn    0.06649237 1.068753          1
## RPS6.R2Gn      0.06600052 1.068227          1
## ATM.RPPA       0.05388770 1.055366          1
## CTNNB1.R2Gn    0.05815197 1.059876          1
## PDCD4.RPPA     0.06301900 1.065047          1
## hsa-mir-499    0.04586515 1.046933          1
## hsa-mir-122    0.03357344 1.034143          1
## TUBA1B.R2Gn    0.02909487 1.029522          1
## ADAR.R2Gn      0.04145008 1.042321          1
## MSH6.RPPA      0.04707180 1.048197          1
## CCND1.R2Gn     0.04373390 1.044704          1
## hsa-mir-137    0.02649739 1.026852          1
## hsa-mir-135a-2 0.03450445 1.035107          1
## hsa-mir-124-3  0.03233885 1.032867          1
## hsa-mir-34b    0.03504260 1.035664          1
## hsa-mir-3662   0.03207855 1.032599          1
## hsa-mir-124-2  0.02809583 1.028494          1
## RBM15.RPPA     0.02392128 1.024210          1
## hsa-mir-200a   0.02864010 1.029054          1
## hsa-mir-216a   0.02667264 1.027032          1
## PEA15.RPPA     0.02553695 1.025866          1
## PTEN.RPPA      0.02205928 1.022304          1
## hsa-mir-1269   0.02359496 1.023876          1
## hsa-mir-3166   0.02476510 1.025074          1
Colors.6.plot.a <- rownames(GGM.all.alive.6.order)
Colors.6.plot.a[grep("hsa", rownames(GGM.all.alive.6.order))] <- "miRNASeqGene"
Colors.6.plot.a[grep(".RPPA", rownames(GGM.all.alive.6.order))] <- "RPPA Array"
Colors.6.plot.a[grep(".R2Gn", rownames(GGM.all.alive.6.order))] <- "RNASeq2GeneNorm"
ggplot(GGM.all.alive.6.order, aes(x = reorder(rownames(GGM.all.alive.6.order), -degree), y = degree, color = Colors.6.plot.a)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.all.alive.6.order$degree), linetype = "dashed", color = "red")  +
  # 36th unit: top 5%
  geom_hline(yintercept = GGM.all.alive.6.order[36,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "All Data", guide = guide_axis(angle = 90)) +
  scale_color_manual(values = c("red", "blue", "green3"))+
  ggtitle("Variables sorted by degree, FDR = 1-1e-6, Surviving Patients")

Colors.6.plot.d <- rownames(GGM.all.dead.6.order)
Colors.6.plot.d[grep("hsa", rownames(GGM.all.dead.6.order))] <- "miRNASeqGene"
Colors.6.plot.d[grep(".RPPA", rownames(GGM.all.dead.6.order))] <- "RPPA Array"
Colors.6.plot.d[grep(".R2Gn", rownames(GGM.all.dead.6.order))] <- "RNASeq2GeneNorm"
ggplot(GGM.all.dead.6.order, aes(x = reorder(rownames(GGM.all.dead.6.order), -degree), y = degree, color = Colors.6.plot.d)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.all.dead.6.order$degree), linetype = "dashed", color = "red")  +
  # 36th unit: top 5%
  geom_hline(yintercept = GGM.all.dead.6.order[36,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "All Data", guide = guide_axis(angle = 90)) +
  scale_color_manual(values = c("red", "blue", "green3"))+
  ggtitle("Variables sorted by degree, FDR = 1-1e-6, Deceased Patients")

Save Gaussian Graphical Model Newotk Statistics to file

The variables are order by degree (number of edges connected to variable node)

out.RPPA = list("FDR1e-1" = GGM.RPPA.order)
out.R2Gn = list("FDR1e-6" = GGM.R2Gn.6.order,
                "FDR1e-14" = GGM.R2Gn.min.order)
out.mRSG = list("FDR1e-6" = GGM.mRSG.6.order,
                "FDR1e-13" = GGM.mRSG.min.order)
out.all = list("FDR1e-6" = GGM.all.6.order,
               "FDR1e-14" = GGM.all.min.order)

out.RPPA.alive = list("FDR1e-1" = GGM.RPPA.alive.order)
out.RPPA.dead = list("FDR1e-1" = GGM.RPPA.dead.order)

out.R2Gn.alive = list("FDR1e-6" = GGM.R2Gn.alive.6.order,
                      "FDR1e-14" = GGM.R2Gn.alive.min.order)
out.R2Gn.dead = list("FDR1e-6" = GGM.R2Gn.dead.6.order,
                      "FDR1e-14" = GGM.R2Gn.dead.min.order)
#out.PR.alive = list("FDR1e-6" = GGM.PR.alive.6.order,
#                    "FDR1e-14" = GGM.PR.alive.min.order)
#out.PR.dead = list("FDR1e-6" = GGM.PR.dead.6.order,
#                   "FDR1e-14" = GGM.PR.dead.min.order)
out.mRSG.alive = list("FDR1e-6" = GGM.mRSG.alive.6.order,
                      "FDR1e-13" = GGM.mRSG.alive.min.order)
out.mRSG.dead = list("FDR1e-6" = GGM.mRSG.dead.6.order,
                      "FDR1e-13" = GGM.mRSG.dead.min.order)
out.all.alive = list("FDR1e-6" = GGM.all.alive.6.order,
                    "FDR1e-14" = GGM.all.alive.min.order)
out.all.dead = list("FDR1e-6" = GGM.all.dead.6.order,
                   "FDR1e-14" = GGM.all.dead.min.order)

out.RPPA.ad = list("alive" = out.RPPA.alive,
                   "dead" = out.RPPA.dead)
out.R2Gn.ad = list("alive" = out.R2Gn.alive,
                   "dead" = out.R2Gn.dead)
#out.PR.ad = list("alive" = out.PR.alive,
#                 "dead" = out.PR.dead)
out.mRSG.ad = list("alive" = out.mRSG.alive,
                   "dead" = out.mRSG.dead)
out.all.ad = list("alive" = out.all.alive,
                   "dead" = out.all.dead)

out.fullsample = list("RPPA Array" = out.RPPA,
                   "RNASeq2GeneNorm" = out.R2Gn,
                   "miRNASeqGene" = out.mRSG,
                   "all" = out.all)

out.splitbyad = list("RPPA Array" = out.RPPA.ad,
                   "RNASeq2GeneNorm" = out.R2Gn.ad,
                   #"RPPA + R2Gn" = out.PR.ad)
                   "miRNASeqGene" = out.mRSG.ad,
                   "all" = out.all.ad)

out.ggm = list("full_sample" = out.fullsample,
           "by_vital_status" = out.splitbyad)

saveRDS(out.ggm, file = "data/sel_features/results_ggm_network_stats_all.rds")

The code below are not executed.

The below was a replacement for the “all dataset search” as optimal lambda was once considered not found for deceased patients , for miRNASeqGene and all data. They are no longer needed when lambda can be found with lambdaMax set to 10 instead of 1000.

F. RPPA Array + RNASeq2GeneNorm

data.PR = data.numeric[, c(group.RPPA, group.R2Gn)]
data.PR.alive = data.PR[which(data.Y == 0), ]
data.PR.dead = data.PR[which(data.Y == 1), ]
set.seed(42)
opt.PR.alive = optPenalty.kCVauto(Y = data.PR.alive, lambdaMin = 1e-11, lambdaMax = 10)
opt.PR.dead = optPenalty.kCVauto(Y = data.PR.dead, lambdaMin = 1e-11, lambdaMax = 10)
opt.PR.alive$optLambda
opt.PR.dead$optLambda
edgeHeat(opt.PR.alive$optPrec, diag = F, textsize = 1)
edgeHeat(opt.PR.dead$optPrec, diag = F, textsize = 1)

Smallest possible FDRcut:

P0.PR.alive.min = sparsify(opt.PR.alive$optPrec, threshold = "localFDR", FDRcut=1-1e-14)
P0.PR.dead.min = sparsify(opt.PR.dead$optPrec, threshold = "localFDR", FDRcut=1-1e-14)

Colors for the different omics:

PcorP.PR.a.min = pruneMatrix(P0.PR.alive.min$sparseParCor)
Colors.PR.a.min = rownames(PcorP.PR.a.min)
Colors.PR.a.min[grep(".RPPA", rownames(PcorP.PR.a.min))] <- "green"
Colors.PR.a.min[grep(".R2Gn", rownames(PcorP.PR.a.min))] <- "cyan"
PcorP.PR.d.min = pruneMatrix(P0.PR.dead.min$sparseParCor)
Colors.PR.d.min = rownames(PcorP.PR.d.min)
Colors.PR.d.min[grep(".RPPA", rownames(PcorP.PR.d.min))] <- "green"
Colors.PR.d.min[grep(".R2Gn", rownames(PcorP.PR.d.min))] <- "cyan"
#fig.width=20, fig.height=20}
#dev.new(width = 20, height = 20, unit = "in", noRstudioGD = F)
set.seed(42)
Ugraph(PcorP.PR.a.min, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
       Vcolor = Colors.PR.a.min,
       main = "RPPA Array + RNASeq2GeneNorm data (Surviving Patients)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\n Blue: RNASeq2Gene; Green:RPPA Array")
Ugraph(PcorP.PR.d.min, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
       Vcolor = Colors.PR.d.min,
       main = "RPPA Array + RNASeq2GeneNorm data (Deceased Patients)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\n Blue: RNASeq2Gene; Green:RPPA Array")

#Ugraph(P0.PR.alive.min$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
#       Vcolor = Colors.PR,
#       main = "RPPA Array + RNASeq2GeneNorm data (Surviving Patiens)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\\n Blue: RNASeq2Gene; Green:RPPA Array")
#Ugraph(P0.PR.dead.min$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
#       Vcolor = Colors.PR,
#       main = "RPPA Array + RNASeq2GeneNorm data (Deceased Patiens)\nFDRcutoff at 1-1e-14, Strong Edge cutoff at 0.5\n Blue: RNASeq2Gene; Green:RPPA Array")
GGM.PR.alive.min = as.data.frame(GGMnetworkStats(P0.PR.alive.min$sparseParCor, as.table = T))
GGM.PR.dead.min = as.data.frame(GGMnetworkStats(P0.PR.dead.min$sparseParCor, as.table = T))
GGM.PR.alive.min.order = GGM.PR.alive.min[order(GGM.PR.alive.min$degree, decreasing = T), ]
GGM.PR.dead.min.order = GGM.PR.dead.min[order(GGM.PR.dead.min$degree, decreasing = T), ]

#Output top 5%
GGM.PR.alive.min.order[1:round(nrow(GGM.PR.alive.min.order) * 0.05), ]
GGM.PR.dead.min.order[1:round(nrow(GGM.PR.dead.min.order) * 0.05), ]
Colors.min.plot.a <- rownames(GGM.PR.alive.min.order)
#Colors.min.plot.a[grep("hsa", rownames(GGM.PR.alive.min.order))] <- "red"
Colors.min.plot.a[grep(".RPPA", rownames(GGM.PR.alive.min.order))] <- "RPPA Array"
Colors.min.plot.a[grep(".R2Gn", rownames(GGM.PR.alive.min.order))] <- "RNASeq2GeneNorm"
#fig.width=24, fig.height=8}
ggplot(GGM.PR.alive.min.order, aes(x = reorder(rownames(GGM.PR.alive.min.order), -degree), y = degree, color = Colors.min.plot.a)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.PR.alive.min.order$degree), linetype = "dashed", color = "red")  +
  # 12th unit: top 5%
  geom_hline(yintercept = GGM.PR.alive.min.order[12,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "RPPA Array + RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
  scale_color_manual(values = c("blue", "green3"))+
  ggtitle("Variables sorted by degree, FDR = 1-1e-14, Surviving Patients")
Colors.min.plot.d <- rownames(GGM.PR.dead.min.order)
#Colors.min.plot.d[grep("hsa", rownames(GGM.PR.dead.min.order))] <- "red"
Colors.min.plot.d[grep(".RPPA", rownames(GGM.PR.dead.min.order))] <- "RPPA Array"
Colors.min.plot.d[grep(".R2Gn", rownames(GGM.PR.dead.min.order))] <- "RNASeq2GeneNorm"
#fig.width=24, fig.height=8}
ggplot(GGM.PR.dead.min.order, aes(x = reorder(rownames(GGM.PR.dead.min.order), -degree), y = degree, color = Colors.min.plot.d)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.PR.dead.min.order$degree), linetype = "dashed", color = "red")  +
  # 12th unit: top 5%
  geom_hline(yintercept = GGM.PR.dead.min.order[12,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "RPPA Array + RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
  scale_color_manual(values = c("blue", "green3"))+
  ggtitle("Variables sorted by degree, FDR = 1-1e-14, Deceased Patients")

FDRcut 1e-6:

P0.PR.alive.6 = sparsify(opt.PR.alive$optPrec, threshold = "localFDR", FDRcut=1-1e-6)
P0.PR.dead.6 = sparsify(opt.PR.dead$optPrec, threshold = "localFDR", FDRcut=1-1e-6)
PcorP.PR.a.6 = pruneMatrix(P0.PR.alive.6$sparseParCor)
Colors.PR.a.6 = rownames(PcorP.PR.a.6)
Colors.PR.a.6[grep(".RPPA", rownames(PcorP.PR.a.6))] <- "green"
Colors.PR.a.6[grep(".R2Gn", rownames(PcorP.PR.a.6))] <- "cyan"
PcorP.PR.d.6 = pruneMatrix(P0.PR.dead.6$sparseParCor)
Colors.PR.d.6 = rownames(PcorP.PR.d.6)
Colors.PR.d.6[grep(".RPPA", rownames(PcorP.PR.d.6))] <- "green"
Colors.PR.d.6[grep(".R2Gn", rownames(PcorP.PR.d.6))] <- "cyan"
# fig.width=20, fig.height=20}
#dev.new(width = 20, height = 20, unit = "in", noRstudioGD = F)
set.seed(42)
Ugraph(PcorP.PR.a.6, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
       Vcolor = Colors.PR.a.6,
       main = "RPPA Array + RNASeq2GeneNorm data (Surviving Patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\n Blue: RNASeq2Gene; Green:RPPA Array")
Ugraph(PcorP.PR.d.6, type = "fancy", lay = "layout_with_fr", Vsize = 5, Vcex = .4, prune = T, cut = 0.5,
       Vcolor = Colors.PR.d.6,
       main = "RPPA Array + RNASeq2GeneNorm data (Deceased Patients)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\n Blue: RNASeq2Gene; Green:RPPA Array")

#Ugraph(P0.PR.alive.6$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
#       Vcolor = Colors.PR,
#       main = "RPPA Array + RNASeq2GeneNorm data (Surviving Patiens)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\\n Blue: RNASeq2Gene; Green:RPPA Array")
#Ugraph(P0.PR.dead.6$sparseParCor, type = "fancy", lay = "layout_in_circle", Vsize = 5, Vcex = .1, prune = T, cut = 0.5,
#       Vcolor = Colors.PR,
#       main = "RPPA Array + RNASeq2GeneNorm data (Deceased Patiens)\nFDRcutoff at 1-1e-6, Strong Edge cutoff at 0.5\n Blue: RNASeq2Gene; Green:RPPA Array")
GGM.PR.alive.6 = as.data.frame(GGMnetworkStats(P0.PR.alive.6$sparseParCor, as.table = T))
GGM.PR.dead.6 = as.data.frame(GGMnetworkStats(P0.PR.dead.6$sparseParCor, as.table = T))
GGM.PR.alive.6.order = GGM.PR.alive.6[order(GGM.PR.alive.6$degree, decreasing = T), ]
GGM.PR.dead.6.order = GGM.PR.dead.6[order(GGM.PR.dead.6$degree, decreasing = T), ]

#Output top 5%
GGM.PR.alive.6.order[1:round(nrow(GGM.PR.alive.6.order) * 0.05), ]
GGM.PR.dead.6.order[1:round(nrow(GGM.PR.dead.6.order) * 0.05), ]
Colors.6.plot.a <- rownames(GGM.PR.alive.6.order)
#Colors.6.plot.a[grep("hsa", rownames(GGM.PR.alive.6.order))] <- "red"
Colors.6.plot.a[grep(".RPPA", rownames(GGM.PR.alive.6.order))] <- "RPPA Array"
Colors.6.plot.a[grep(".R2Gn", rownames(GGM.PR.alive.6.order))] <- "RNASeq2GeneNorm"
#fig.width=24, fig.height=8}
ggplot(GGM.PR.alive.6.order, aes(x = reorder(rownames(GGM.PR.alive.6.order), -degree), y = degree, color = Colors.6.plot.a)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.PR.alive.6.order$degree), linetype = "dashed", color = "red")  +
  # 12th unit: top 5%
  geom_hline(yintercept = GGM.PR.alive.6.order[12,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "RPPA Array + RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
  scale_color_manual(values = c("blue", "green3"))+
  ggtitle("Variables sorted by degree, FDR = 1-1e-6, Surviving Patients")
Colors.6.plot.d <- rownames(GGM.PR.dead.6.order)
#Colors.6.plot.d[grep("hsa", rownames(GGM.PR.dead.6.order))] <- "red"
Colors.6.plot.d[grep(".RPPA", rownames(GGM.PR.dead.6.order))] <- "RPPA Array"
Colors.6.plot.d[grep(".R2Gn", rownames(GGM.PR.dead.6.order))] <- "RNASeq2GeneNorm"
#fig.width=24, fig.height=8}
ggplot(GGM.PR.dead.6.order, aes(x = reorder(rownames(GGM.PR.dead.6.order), -degree), y = degree, color = Colors.6.plot.d)) +
  geom_point() +
  geom_hline(yintercept = mean(GGM.PR.dead.6.order$degree), linetype = "dashed", color = "red")  +
  # 12th unit: top 5%
  geom_hline(yintercept = GGM.PR.dead.6.order[12,]$degree, linetype = "dashed", color = "darkgreen")  +
  scale_x_discrete(name = "RPPA Array + RNASeq2GeneNorm", guide = guide_axis(angle = 90)) +
  scale_color_manual(values = c("blue", "green3"))+
  ggtitle("Variables sorted by degree, FDR = 1-1e-6, Deceased Patients")